diff --git a/README.md b/README.md index 68202ba095..07334416ed 100644 --- a/README.md +++ b/README.md @@ -171,7 +171,7 @@ Also, say ๐Ÿ‘‹ in our public Discord channel Text-guided Image Inpainting Stable Diffusion Inpainting - runwayml/stable-diffusion-inpainting + stable-diffusion-v1-5/stable-diffusion-inpainting Image Variation diff --git a/docs/source/en/api/models/asymmetricautoencoderkl.md b/docs/source/en/api/models/asymmetricautoencoderkl.md index fbadf9bd40..0c7fbd38fb 100644 --- a/docs/source/en/api/models/asymmetricautoencoderkl.md +++ b/docs/source/en/api/models/asymmetricautoencoderkl.md @@ -39,7 +39,7 @@ mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images original_image = load_image(img_url).resize((512, 512)) mask_image = load_image(mask_url).resize((512, 512)) -pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") +pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting") pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") pipe.to("cuda") diff --git a/docs/source/en/api/pipelines/stable_diffusion/depth2img.md b/docs/source/en/api/pipelines/stable_diffusion/depth2img.md index aa43cf7db9..9ac9b7f906 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/depth2img.md +++ b/docs/source/en/api/pipelines/stable_diffusion/depth2img.md @@ -21,7 +21,7 @@ The Stable Diffusion model can also infer depth based on an image using [MiDaS]( > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## StableDiffusionDepth2ImgPipeline diff --git a/docs/source/en/api/pipelines/stable_diffusion/inpaint.md b/docs/source/en/api/pipelines/stable_diffusion/inpaint.md index 84cc31e158..d60f472d0a 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/inpaint.md +++ b/docs/source/en/api/pipelines/stable_diffusion/inpaint.md @@ -21,14 +21,14 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit ## Tips It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such -as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default +as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default text-to-image Stable Diffusion checkpoints, such as [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) are also compatible but they might be less performant. > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## StableDiffusionInpaintPipeline diff --git a/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md b/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md index 4f0521740c..19eae9a9ce 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md +++ b/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md @@ -17,7 +17,7 @@ The Stable Diffusion latent upscaler model was created by [Katherine Crowson](ht > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## StableDiffusionLatentUpscalePipeline diff --git a/docs/source/en/api/pipelines/stable_diffusion/overview.md b/docs/source/en/api/pipelines/stable_diffusion/overview.md index 7e6e16c347..2d2de39c91 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/overview.md +++ b/docs/source/en/api/pipelines/stable_diffusion/overview.md @@ -22,7 +22,7 @@ Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B data For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI [announcement](https://stability.ai/blog/stable-diffusion-announcement) and our own [blog post](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) for more technical details. -You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case! +You can find the original codebase for Stable Diffusion v1.0 at [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) and Stable Diffusion v2.0 at [Stability-AI/stablediffusion](https://github.com/Stability-AI/stablediffusion) as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations. Explore these organizations to find the best checkpoint for your use-case! The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo: @@ -64,7 +64,7 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo StableDiffusionInpaint inpainting - + diff --git a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md index 67729cd195..31164fb9ae 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md +++ b/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md @@ -36,7 +36,7 @@ Here are some examples for how to use Stable Diffusion 2 for each task: > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## Text-to-image diff --git a/docs/source/en/api/pipelines/stable_diffusion/text2img.md b/docs/source/en/api/pipelines/stable_diffusion/text2img.md index 59a0f00d22..2033638a1a 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/text2img.md +++ b/docs/source/en/api/pipelines/stable_diffusion/text2img.md @@ -25,7 +25,7 @@ The abstract from the paper is: > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## StableDiffusionPipeline diff --git a/docs/source/en/api/pipelines/stable_diffusion/upscale.md b/docs/source/en/api/pipelines/stable_diffusion/upscale.md index 14393370be..e7d571e73c 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/upscale.md +++ b/docs/source/en/api/pipelines/stable_diffusion/upscale.md @@ -21,7 +21,7 @@ The Stable Diffusion upscaler diffusion model was created by the researchers and > [!TIP] > Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! > -> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! +> If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis) and [Stability AI](https://huggingface.co/stabilityai) Hub organizations! ## StableDiffusionUpscalePipeline diff --git a/docs/source/en/training/adapt_a_model.md b/docs/source/en/training/adapt_a_model.md index f528c8bfb6..9b7efd2abf 100644 --- a/docs/source/en/training/adapt_a_model.md +++ b/docs/source/en/training/adapt_a_model.md @@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"] 4 ``` -Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting): +Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting): ```py from diffusers import StableDiffusionPipeline -pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True) +pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True) pipeline.unet.config["in_channels"] 9 ``` diff --git a/docs/source/en/using-diffusers/inference_with_lcm.md b/docs/source/en/using-diffusers/inference_with_lcm.md index cde4168d38..258ed2979a 100644 --- a/docs/source/en/using-diffusers/inference_with_lcm.md +++ b/docs/source/en/using-diffusers/inference_with_lcm.md @@ -215,7 +215,7 @@ from diffusers import AutoPipelineForInpainting, LCMScheduler from diffusers.utils import load_image, make_image_grid pipe = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16", ).to("cuda") diff --git a/docs/source/en/using-diffusers/inpaint.md b/docs/source/en/using-diffusers/inpaint.md index 28da3a68a5..232dbf2c6b 100644 --- a/docs/source/en/using-diffusers/inpaint.md +++ b/docs/source/en/using-diffusers/inpaint.md @@ -112,7 +112,7 @@ blurred_mask ## Popular models -[Stable Diffusion Inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images. +[Stable Diffusion Inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting), [Stable Diffusion XL (SDXL) Inpainting](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1), and [Kandinsky 2.2 Inpainting](https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder-inpaint) are among the most popular models for inpainting. SDXL typically produces higher resolution images than Stable Diffusion v1.5, and Kandinsky 2.2 is also capable of generating high-quality images. ### Stable Diffusion Inpainting @@ -124,7 +124,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -244,7 +244,7 @@ make_image_grid([init_image, image], rows=1, cols=2) ``` - + ```py import torch @@ -252,7 +252,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -278,7 +278,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
-
runwayml/stable-diffusion-inpainting
+
stable-diffusion-v1-5/stable-diffusion-inpainting
@@ -308,7 +308,7 @@ make_image_grid([init_image, image], rows=1, cols=2) ``` - + ```py import torch @@ -316,7 +316,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -340,7 +340,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
-
runwayml/stable-diffusion-inpainting
+
stable-diffusion-v1-5/stable-diffusion-inpainting
@@ -358,7 +358,7 @@ from diffusers.utils import load_image, make_image_grid device = "cuda" pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) @@ -396,7 +396,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -441,7 +441,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -481,7 +481,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -606,7 +606,7 @@ from diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image from diffusers.utils import load_image, make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -683,7 +683,7 @@ from diffusers import AutoPipelineForInpainting from diffusers.utils import make_image_grid pipeline = AutoPipelineForInpainting.from_pretrained( - "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed @@ -714,7 +714,7 @@ controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpai # pass ControlNet to the pipeline pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16" + "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16" ) pipeline.enable_model_cpu_offload() # remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed diff --git a/docs/source/ko/optimization/torch2.0.md b/docs/source/ko/optimization/torch2.0.md index c78c4a87b6..354f7243cf 100644 --- a/docs/source/ko/optimization/torch2.0.md +++ b/docs/source/ko/optimization/torch2.0.md @@ -173,7 +173,7 @@ mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data init_image = download_image(img_url).resize((512, 512)) mask_image = download_image(mask_url).resize((512, 512)) -path = "runwayml/stable-diffusion-inpainting" +path = "stable-diffusion-v1-5/stable-diffusion-inpainting" run_compile = True # Set True / False diff --git a/docs/source/ko/training/adapt_a_model.md b/docs/source/ko/training/adapt_a_model.md index 3795558f5f..fe6fde05b7 100644 --- a/docs/source/ko/training/adapt_a_model.md +++ b/docs/source/ko/training/adapt_a_model.md @@ -28,12 +28,12 @@ pipeline.unet.config["in_channels"] 4 ``` -์ธํŽ˜์ธํŒ…์€ ์ž…๋ ฅ ์ƒ˜ํ”Œ์— 9๊ฐœ์˜ ์ฑ„๋„์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)์™€ ๊ฐ™์€ ์‚ฌ์ „ํ•™์Šต๋œ ์ธํŽ˜์ธํŒ… ๋ชจ๋ธ์—์„œ ์ด ๊ฐ’์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค: +์ธํŽ˜์ธํŒ…์€ ์ž…๋ ฅ ์ƒ˜ํ”Œ์— 9๊ฐœ์˜ ์ฑ„๋„์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)์™€ ๊ฐ™์€ ์‚ฌ์ „ํ•™์Šต๋œ ์ธํŽ˜์ธํŒ… ๋ชจ๋ธ์—์„œ ์ด ๊ฐ’์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค: ```py from diffusers import StableDiffusionPipeline -pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") +pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting") pipeline.unet.config["in_channels"] 9 ``` diff --git a/docs/source/ko/using-diffusers/inpaint.md b/docs/source/ko/using-diffusers/inpaint.md index cefb892186..6c0c08bf73 100644 --- a/docs/source/ko/using-diffusers/inpaint.md +++ b/docs/source/ko/using-diffusers/inpaint.md @@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License. [[open-in-colab]] -[`StableDiffusionInpaintPipeline`]์€ ๋งˆ์Šคํฌ์™€ ํ…์ŠคํŠธ ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์ด๋ฏธ์ง€์˜ ํŠน์ • ๋ถ€๋ถ„์„ ํŽธ์ง‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ฉ๋‹ˆ๋‹ค. ์ด ๊ธฐ๋Šฅ์€ ์ธํŽ˜์ธํŒ… ์ž‘์—…์„ ์œ„ํ•ด ํŠน๋ณ„ํžˆ ํ›ˆ๋ จ๋œ [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)๊ณผ ๊ฐ™์€ Stable Diffusion ๋ฒ„์ „์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. +[`StableDiffusionInpaintPipeline`]์€ ๋งˆ์Šคํฌ์™€ ํ…์ŠคํŠธ ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ์ด๋ฏธ์ง€์˜ ํŠน์ • ๋ถ€๋ถ„์„ ํŽธ์ง‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ฉ๋‹ˆ๋‹ค. ์ด ๊ธฐ๋Šฅ์€ ์ธํŽ˜์ธํŒ… ์ž‘์—…์„ ์œ„ํ•ด ํŠน๋ณ„ํžˆ ํ›ˆ๋ จ๋œ [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)๊ณผ ๊ฐ™์€ Stable Diffusion ๋ฒ„์ „์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. ๋จผ์ € [`StableDiffusionInpaintPipeline`] ์ธ์Šคํ„ด์Šค๋ฅผ ๋ถˆ๋Ÿฌ์˜ต๋‹ˆ๋‹ค: @@ -27,7 +27,7 @@ from io import BytesIO from diffusers import StableDiffusionInpaintPipeline pipeline = StableDiffusionInpaintPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, ) pipeline = pipeline.to("cuda") @@ -61,12 +61,3 @@ image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0] > [!WARNING] > ์ด์ „์˜ ์‹คํ—˜์ ์ธ ์ธํŽ˜์ธํŒ… ๊ตฌํ˜„์—์„œ๋Š” ํ’ˆ์งˆ์ด ๋‚ฎ์€ ๋‹ค๋ฅธ ํ”„๋กœ์„ธ์Šค๋ฅผ ์‚ฌ์šฉํ–ˆ์Šต๋‹ˆ๋‹ค. ์ด์ „ ๋ฒ„์ „๊ณผ์˜ ํ˜ธํ™˜์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ ๋ชจ๋ธ์ด ํฌํ•จ๋˜์ง€ ์•Š์€ ์‚ฌ์ „ํ•™์Šต๋œ ํŒŒ์ดํ”„๋ผ์ธ์„ ๋ถˆ๋Ÿฌ์˜ค๋ฉด ์ด์ „ ์ธํŽ˜์ธํŒ… ๋ฐฉ๋ฒ•์ด ๊ณ„์† ์ ์šฉ๋ฉ๋‹ˆ๋‹ค. - -์•„๋ž˜ Space์—์„œ ์ด๋ฏธ์ง€ ์ธํŽ˜์ธํŒ…์„ ์ง์ ‘ ํ•ด๋ณด์„ธ์š”! - - diff --git a/docs/source/zh/training/adapt_a_model.md b/docs/source/zh/training/adapt_a_model.md index b5f9155697..7dbf46ec12 100644 --- a/docs/source/zh/training/adapt_a_model.md +++ b/docs/source/zh/training/adapt_a_model.md @@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"] 4 ``` -่€Œๅ›พๅƒไฟฎๅคไปปๅŠก้œ€่ฆ่พ“ๅ…ฅๆ ทๆœฌๅ…ทๆœ‰9ไธช้€š้“ใ€‚ๆ‚จๅฏไปฅๅœจ [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) ่ฟ™ๆ ท็š„้ข„่ฎญ็ปƒไฟฎๅคๆจกๅž‹ไธญ้ชŒ่ฏๆญคๅ‚ๆ•ฐ๏ผš +่€Œๅ›พๅƒไฟฎๅคไปปๅŠก้œ€่ฆ่พ“ๅ…ฅๆ ทๆœฌๅ…ทๆœ‰9ไธช้€š้“ใ€‚ๆ‚จๅฏไปฅๅœจ [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting) ่ฟ™ๆ ท็š„้ข„่ฎญ็ปƒไฟฎๅคๆจกๅž‹ไธญ้ชŒ่ฏๆญคๅ‚ๆ•ฐ๏ผš ```python from diffusers import StableDiffusionPipeline -pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True) +pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True) pipeline.unet.config["in_channels"] 9 ``` diff --git a/examples/community/README.md b/examples/community/README.md index e314463077..4a4b0f5fd9 100644 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -1328,7 +1328,7 @@ model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined" # Load Stable Diffusion Inpainting Pipeline with custom pipeline pipe = DiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", + "stable-diffusion-v1-5/stable-diffusion-inpainting", custom_pipeline="text_inpainting", segmentation_model=model, segmentation_processor=processor diff --git a/examples/community/adaptive_mask_inpainting.py b/examples/community/adaptive_mask_inpainting.py index aac460cb46..da67debe72 100644 --- a/examples/community/adaptive_mask_inpainting.py +++ b/examples/community/adaptive_mask_inpainting.py @@ -126,7 +126,7 @@ EXAMPLE_DOC_STRING = """ ... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16 ... ) >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) @@ -347,7 +347,7 @@ class AdaptiveMaskInpaintPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -429,8 +429,8 @@ class AdaptiveMaskInpaintPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" @@ -970,7 +970,7 @@ class AdaptiveMaskInpaintPipeline( >>> default_mask_image = download_image(mask_url).resize((512, 512)) >>> pipe = AdaptiveMaskInpaintPipeline.from_pretrained( - ... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") @@ -1095,7 +1095,7 @@ class AdaptiveMaskInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/composable_stable_diffusion.py b/examples/community/composable_stable_diffusion.py index a7c540ceb9..a7c610ad43 100644 --- a/examples/community/composable_stable_diffusion.py +++ b/examples/community/composable_stable_diffusion.py @@ -62,7 +62,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin) [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -145,8 +145,8 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin) "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/fresco_v2v.py b/examples/community/fresco_v2v.py index 47ba71299d..b79834db5e 100644 --- a/examples/community/fresco_v2v.py +++ b/examples/community/fresco_v2v.py @@ -1276,7 +1276,7 @@ class FrescoV2VPipeline(StableDiffusionControlNetImg2ImgPipeline): [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/community/hd_painter.py b/examples/community/hd_painter.py index 20bb43a76f..70e5656855 100644 --- a/examples/community/hd_painter.py +++ b/examples/community/hd_painter.py @@ -678,7 +678,7 @@ class StableDiffusionHDPainterPipeline(StableDiffusionInpaintPipeline): # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/img2img_inpainting.py b/examples/community/img2img_inpainting.py index 499230b1e2..595df107ca 100644 --- a/examples/community/img2img_inpainting.py +++ b/examples/community/img2img_inpainting.py @@ -78,7 +78,7 @@ class ImageToImageInpaintingPipeline(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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/instaflow_one_step.py b/examples/community/instaflow_one_step.py index 06be1d10b6..0f16707ead 100644 --- a/examples/community/instaflow_one_step.py +++ b/examples/community/instaflow_one_step.py @@ -86,7 +86,7 @@ class InstaFlowPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -165,8 +165,8 @@ class InstaFlowPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/ip_adapter_face_id.py b/examples/community/ip_adapter_face_id.py index 5b420882e9..d16aaf5a54 100644 --- a/examples/community/ip_adapter_face_id.py +++ b/examples/community/ip_adapter_face_id.py @@ -166,7 +166,7 @@ class IPAdapterFaceIDStableDiffusionPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -247,8 +247,8 @@ class IPAdapterFaceIDStableDiffusionPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/kohya_hires_fix.py b/examples/community/kohya_hires_fix.py index 63f6b8973c..c968ecf2af 100644 --- a/examples/community/kohya_hires_fix.py +++ b/examples/community/kohya_hires_fix.py @@ -414,7 +414,7 @@ class StableDiffusionHighResFixPipeline(StableDiffusionPipeline): [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/community/latent_consistency_interpolate.py b/examples/community/latent_consistency_interpolate.py index 9fc4233682..e8349ba317 100644 --- a/examples/community/latent_consistency_interpolate.py +++ b/examples/community/latent_consistency_interpolate.py @@ -222,7 +222,7 @@ class LatentConsistencyModelWalkPipeline( supports [`LCMScheduler`]. 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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/community/llm_grounded_diffusion.py b/examples/community/llm_grounded_diffusion.py index 8f04761502..5bf6674a43 100644 --- a/examples/community/llm_grounded_diffusion.py +++ b/examples/community/llm_grounded_diffusion.py @@ -302,7 +302,7 @@ class LLMGroundedDiffusionPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -392,8 +392,8 @@ class LLMGroundedDiffusionPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/lpw_stable_diffusion.py b/examples/community/lpw_stable_diffusion.py index cb017c0bbe..58e932bbcf 100644 --- a/examples/community/lpw_stable_diffusion.py +++ b/examples/community/lpw_stable_diffusion.py @@ -552,8 +552,8 @@ class StableDiffusionLongPromptWeightingPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py index 272c5d5652..95d3405df5 100644 --- a/examples/community/lpw_stable_diffusion_xl.py +++ b/examples/community/lpw_stable_diffusion_xl.py @@ -1765,7 +1765,7 @@ class SDXLLongPromptWeightingPipeline( # Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet: diff --git a/examples/community/matryoshka.py b/examples/community/matryoshka.py index 3871552672..97ad8b9e86 100644 --- a/examples/community/matryoshka.py +++ b/examples/community/matryoshka.py @@ -3729,8 +3729,8 @@ class MatryoshkaPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/multilingual_stable_diffusion.py b/examples/community/multilingual_stable_diffusion.py index afef4e9e97..436803f201 100644 --- a/examples/community/multilingual_stable_diffusion.py +++ b/examples/community/multilingual_stable_diffusion.py @@ -78,7 +78,7 @@ class MultilingualStableDiffusion(DiffusionPipeline, StableDiffusionMixin): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py index 4b6123cc1f..3abd984829 100644 --- a/examples/community/pipeline_controlnet_xl_kolors_inpaint.py +++ b/examples/community/pipeline_controlnet_xl_kolors_inpaint.py @@ -1607,7 +1607,7 @@ class KolorsControlNetInpaintPipeline( # 9. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/pipeline_fabric.py b/examples/community/pipeline_fabric.py index dcc7730cbe..d29e98df5e 100644 --- a/examples/community/pipeline_fabric.py +++ b/examples/community/pipeline_fabric.py @@ -135,7 +135,7 @@ class FabricPipeline(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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. """ @@ -163,8 +163,8 @@ class FabricPipeline(DiffusionPipeline): "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/pipeline_kolors_inpainting.py b/examples/community/pipeline_kolors_inpainting.py index 3cab8ecac0..26517819eb 100644 --- a/examples/community/pipeline_kolors_inpainting.py +++ b/examples/community/pipeline_kolors_inpainting.py @@ -1487,7 +1487,7 @@ class KolorsInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/pipeline_prompt2prompt.py b/examples/community/pipeline_prompt2prompt.py index 8d94dc9248..eb19667970 100644 --- a/examples/community/pipeline_prompt2prompt.py +++ b/examples/community/pipeline_prompt2prompt.py @@ -106,7 +106,7 @@ class Prompt2PromptPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -187,8 +187,8 @@ class Prompt2PromptPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/pipeline_sdxl_style_aligned.py b/examples/community/pipeline_sdxl_style_aligned.py index 10438af365..51547599f5 100644 --- a/examples/community/pipeline_sdxl_style_aligned.py +++ b/examples/community/pipeline_sdxl_style_aligned.py @@ -1730,7 +1730,7 @@ class StyleAlignedSDXLPipeline( # Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != num_channels_unet: diff --git a/examples/community/pipeline_stable_diffusion_boxdiff.py b/examples/community/pipeline_stable_diffusion_boxdiff.py index 07e29b9c05..c05a614313 100644 --- a/examples/community/pipeline_stable_diffusion_boxdiff.py +++ b/examples/community/pipeline_stable_diffusion_boxdiff.py @@ -59,7 +59,7 @@ EXAMPLE_DOC_STRING = """ >>> import torch >>> from diffusers import StableDiffusionPipeline - >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" @@ -392,7 +392,7 @@ class StableDiffusionBoxDiffPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -473,8 +473,8 @@ class StableDiffusionBoxDiffPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/pipeline_stable_diffusion_pag.py b/examples/community/pipeline_stable_diffusion_pag.py index 6b62b610af..3f98dca0b9 100644 --- a/examples/community/pipeline_stable_diffusion_pag.py +++ b/examples/community/pipeline_stable_diffusion_pag.py @@ -42,7 +42,7 @@ EXAMPLE_DOC_STRING = """ ```py >>> import torch >>> from diffusers import StableDiffusionPipeline - >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] @@ -359,7 +359,7 @@ class StableDiffusionPAGPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. @@ -440,8 +440,8 @@ class StableDiffusionPAGPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py index 9777633535..e358f66b4a 100644 --- a/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py +++ b/examples/community/pipeline_stable_diffusion_upscale_ldm3d.py @@ -100,7 +100,7 @@ class StableDiffusionUpscaleLDM3DPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py index a881814c2a..65df4c03eb 100644 --- a/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py +++ b/examples/community/pipeline_stable_diffusion_xl_attentive_eraser.py @@ -2042,7 +2042,7 @@ class StableDiffusionXL_AE_Pipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py index 564a19e923..6dade126f2 100644 --- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py +++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter.py @@ -188,7 +188,7 @@ class StableDiffusionXLControlNetAdapterPipeline( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py index c73433b20f..9ec6a90b07 100644 --- a/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py +++ b/examples/community/pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py @@ -330,7 +330,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`): @@ -1569,7 +1569,7 @@ class StableDiffusionXLControlNetAdapterInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/examples/community/pipeline_zero1to3.py b/examples/community/pipeline_zero1to3.py index 9e29566978..1be59fd832 100644 --- a/examples/community/pipeline_zero1to3.py +++ b/examples/community/pipeline_zero1to3.py @@ -46,7 +46,7 @@ EXAMPLE_DOC_STRING = """ >>> import torch >>> from diffusers import StableDiffusionPipeline - >>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + >>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" @@ -86,7 +86,7 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. cc_projection ([`CCProjection`]): @@ -164,8 +164,8 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin): "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/rerender_a_video.py b/examples/community/rerender_a_video.py index 78a15a03b0..840f9e206d 100644 --- a/examples/community/rerender_a_video.py +++ b/examples/community/rerender_a_video.py @@ -288,7 +288,7 @@ class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/run_onnx_controlnet.py b/examples/community/run_onnx_controlnet.py index f0ab2a2b96..2b56e8a1e5 100644 --- a/examples/community/run_onnx_controlnet.py +++ b/examples/community/run_onnx_controlnet.py @@ -54,7 +54,7 @@ EXAMPLE_DOC_STRING = """ >>> # load control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # speed up diffusion process with faster scheduler and memory optimization diff --git a/examples/community/run_tensorrt_controlnet.py b/examples/community/run_tensorrt_controlnet.py index e4f1abc83b..b62eb4f58e 100644 --- a/examples/community/run_tensorrt_controlnet.py +++ b/examples/community/run_tensorrt_controlnet.py @@ -158,7 +158,7 @@ EXAMPLE_DOC_STRING = """ >>> # load control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 ... ) >>> # speed up diffusion process with faster scheduler and memory optimization diff --git a/examples/community/sd_text2img_k_diffusion.py b/examples/community/sd_text2img_k_diffusion.py index 4d5cea497f..e351420f78 100755 --- a/examples/community/sd_text2img_k_diffusion.py +++ b/examples/community/sd_text2img_k_diffusion.py @@ -64,7 +64,7 @@ class StableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/sde_drag.py b/examples/community/sde_drag.py index f408ee64db..63899ce738 100644 --- a/examples/community/sde_drag.py +++ b/examples/community/sde_drag.py @@ -114,7 +114,7 @@ class SdeDragPipeline(DiffusionPipeline): >>> 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') diff --git a/examples/community/stable_diffusion_comparison.py b/examples/community/stable_diffusion_comparison.py index 22f3b3e0c3..ce6e77c87f 100644 --- a/examples/community/stable_diffusion_comparison.py +++ b/examples/community/stable_diffusion_comparison.py @@ -46,7 +46,7 @@ class StableDiffusionComparisonPipeline(DiffusionPipeline, StableDiffusionMixin) [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/stable_diffusion_controlnet_img2img.py b/examples/community/stable_diffusion_controlnet_img2img.py index 6d8038cfd4..aa116112be 100644 --- a/examples/community/stable_diffusion_controlnet_img2img.py +++ b/examples/community/stable_diffusion_controlnet_img2img.py @@ -36,7 +36,7 @@ EXAMPLE_DOC_STRING = """ >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe_controlnet = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", + "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 diff --git a/examples/community/stable_diffusion_controlnet_inpaint.py b/examples/community/stable_diffusion_controlnet_inpaint.py index fe7b808b6b..6d710e0d73 100644 --- a/examples/community/stable_diffusion_controlnet_inpaint.py +++ b/examples/community/stable_diffusion_controlnet_inpaint.py @@ -81,7 +81,7 @@ EXAMPLE_DOC_STRING = """ >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) diff --git a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py index 2b5dc77fe5..fcb5ed059b 100644 --- a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py +++ b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py @@ -80,7 +80,7 @@ EXAMPLE_DOC_STRING = """ >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16) >>> pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 + "stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) diff --git a/examples/community/stable_diffusion_controlnet_reference.py b/examples/community/stable_diffusion_controlnet_reference.py index e5dd249e04..74c81b6362 100644 --- a/examples/community/stable_diffusion_controlnet_reference.py +++ b/examples/community/stable_diffusion_controlnet_reference.py @@ -37,7 +37,7 @@ EXAMPLE_DOC_STRING = """ >>> 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 diff --git a/examples/community/stable_diffusion_ipex.py b/examples/community/stable_diffusion_ipex.py index 7d1cd4f5d0..4f545aa09d 100644 --- a/examples/community/stable_diffusion_ipex.py +++ b/examples/community/stable_diffusion_ipex.py @@ -43,7 +43,7 @@ EXAMPLE_DOC_STRING = """ >>> import torch >>> from diffusers import StableDiffusionPipeline - >>> 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 @@ -85,7 +85,7 @@ class StableDiffusionIPEXPipeline( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -161,8 +161,8 @@ class StableDiffusionIPEXPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/stable_diffusion_mega.py b/examples/community/stable_diffusion_mega.py index 77e5011d2a..c67ebc80b0 100644 --- a/examples/community/stable_diffusion_mega.py +++ b/examples/community/stable_diffusion_mega.py @@ -47,7 +47,7 @@ class StableDiffusionMegaPipeline(DiffusionPipeline, StableDiffusionMixin): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/community/stable_diffusion_reference.py b/examples/community/stable_diffusion_reference.py index 6f7dce9823..d0372bbeba 100644 --- a/examples/community/stable_diffusion_reference.py +++ b/examples/community/stable_diffusion_reference.py @@ -46,7 +46,7 @@ EXAMPLE_DOC_STRING = """ >>> 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') @@ -112,7 +112,7 @@ class StableDiffusionReferencePipeline( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -194,8 +194,8 @@ class StableDiffusionReferencePipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/stable_diffusion_repaint.py b/examples/community/stable_diffusion_repaint.py index 94b9f8b01b..b974e3c7ae 100644 --- a/examples/community/stable_diffusion_repaint.py +++ b/examples/community/stable_diffusion_repaint.py @@ -167,7 +167,7 @@ class StableDiffusionRepaintPipeline( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -249,8 +249,8 @@ class StableDiffusionRepaintPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/stable_diffusion_tensorrt_img2img.py b/examples/community/stable_diffusion_tensorrt_img2img.py index dc11703b6a..5b7733fe57 100755 --- a/examples/community/stable_diffusion_tensorrt_img2img.py +++ b/examples/community/stable_diffusion_tensorrt_img2img.py @@ -678,7 +678,7 @@ class TensorRTStableDiffusionImg2ImgPipeline(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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -766,8 +766,8 @@ class TensorRTStableDiffusionImg2ImgPipeline(DiffusionPipeline): "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/stable_diffusion_tensorrt_inpaint.py b/examples/community/stable_diffusion_tensorrt_inpaint.py index fff7309e9c..fc81e4c289 100755 --- a/examples/community/stable_diffusion_tensorrt_inpaint.py +++ b/examples/community/stable_diffusion_tensorrt_inpaint.py @@ -682,7 +682,7 @@ class TensorRTStableDiffusionInpaintPipeline(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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -770,8 +770,8 @@ class TensorRTStableDiffusionInpaintPipeline(DiffusionPipeline): "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/stable_diffusion_tensorrt_txt2img.py b/examples/community/stable_diffusion_tensorrt_txt2img.py index 15a6e69c41..e1d09edf93 100755 --- a/examples/community/stable_diffusion_tensorrt_txt2img.py +++ b/examples/community/stable_diffusion_tensorrt_txt2img.py @@ -594,7 +594,7 @@ class TensorRTStableDiffusionPipeline(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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -682,8 +682,8 @@ class TensorRTStableDiffusionPipeline(DiffusionPipeline): "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/examples/community/text_inpainting.py b/examples/community/text_inpainting.py index f262cf2cac..bdf9eca498 100644 --- a/examples/community/text_inpainting.py +++ b/examples/community/text_inpainting.py @@ -52,7 +52,7 @@ class TextInpainting(DiffusionPipeline, StableDiffusionMixin): [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/examples/research_projects/anytext/anytext.py b/examples/research_projects/anytext/anytext.py index 38f0adb891..7ae6ae57c2 100644 --- a/examples/research_projects/anytext/anytext.py +++ b/examples/research_projects/anytext/anytext.py @@ -1223,7 +1223,7 @@ class AnyTextPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/research_projects/dreambooth_inpaint/README.md b/examples/research_projects/dreambooth_inpaint/README.md index 46703fa982..b6ee1d72f6 100644 --- a/examples/research_projects/dreambooth_inpaint/README.md +++ b/examples/research_projects/dreambooth_inpaint/README.md @@ -5,7 +5,7 @@ This script was added by @thedarkzeno . Please note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though. ```bash -export MODEL_NAME="runwayml/stable-diffusion-inpainting" +export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting" export INSTANCE_DIR="path-to-instance-images" export OUTPUT_DIR="path-to-save-model" @@ -29,7 +29,7 @@ Prior-preservation is used to avoid overfitting and language-drift. Refer to the According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases. ```bash -export MODEL_NAME="runwayml/stable-diffusion-inpainting" +export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting" export INSTANCE_DIR="path-to-instance-images" export CLASS_DIR="path-to-class-images" export OUTPUT_DIR="path-to-save-model" @@ -60,7 +60,7 @@ With the help of gradient checkpointing and the 8-bit optimizer from bitsandbyte To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation). ```bash -export MODEL_NAME="runwayml/stable-diffusion-inpainting" +export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting" export INSTANCE_DIR="path-to-instance-images" export CLASS_DIR="path-to-class-images" export OUTPUT_DIR="path-to-save-model" @@ -92,7 +92,7 @@ Pass the `--train_text_encoder` argument to the script to enable training `text_ ___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___ ```bash -export MODEL_NAME="runwayml/stable-diffusion-inpainting" +export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting" export INSTANCE_DIR="path-to-instance-images" export CLASS_DIR="path-to-class-images" export OUTPUT_DIR="path-to-save-model" diff --git a/examples/research_projects/ip_adapter/README.md b/examples/research_projects/ip_adapter/README.md index 3df9644ddf..0bead5ae85 100644 --- a/examples/research_projects/ip_adapter/README.md +++ b/examples/research_projects/ip_adapter/README.md @@ -55,7 +55,7 @@ The Accelerate launch command is used to train a model using multiple GPUs and m ``` accelerate launch --mixed_precision "fp16" \ tutorial_train_ip-adapter.py \ ---pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \ +--pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \ --image_encoder_path="{image_encoder_path}" \ --data_json_file="{data.json}" \ --data_root_path="{image_path}" \ @@ -73,7 +73,7 @@ tutorial_train_ip-adapter.py \ ``` accelerate launch --num_processes 8 --multi_gpu --mixed_precision "fp16" \ tutorial_train_ip-adapter.py \ - --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5/" \ + --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5/" \ --image_encoder_path="{image_encoder_path}" \ --data_json_file="{data.json}" \ --data_root_path="{image_path}" \ diff --git a/examples/research_projects/multi_subject_dreambooth_inpainting/README.md b/examples/research_projects/multi_subject_dreambooth_inpainting/README.md index 8ddef1b83c..3412de662f 100644 --- a/examples/research_projects/multi_subject_dreambooth_inpainting/README.md +++ b/examples/research_projects/multi_subject_dreambooth_inpainting/README.md @@ -27,7 +27,7 @@ You can build multiple datasets for every subject and upload them to the ๐Ÿค— hu Before launching the training script, make sure to select the inpainting the target model, the output directory and the ๐Ÿค— datasets. ```bash -export MODEL_NAME="runwayml/stable-diffusion-inpainting" +export MODEL_NAME="stable-diffusion-v1-5/stable-diffusion-inpainting" export OUTPUT_DIR="path-to-save-model" export DATASET_1="gzguevara/mr_potato_head_masked" diff --git a/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py b/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py index 1bd9c0161f..233df12765 100644 --- a/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py +++ b/examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py @@ -177,7 +177,7 @@ class PromptDiffusionPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/examples/research_projects/vae/vae_roundtrip.py b/examples/research_projects/vae/vae_roundtrip.py index cdc3a54fdf..922cb42615 100644 --- a/examples/research_projects/vae/vae_roundtrip.py +++ b/examples/research_projects/vae/vae_roundtrip.py @@ -238,7 +238,7 @@ def parse_args() -> argparse.Namespace: # EXAMPLE USAGE: # -# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "runwayml/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png" +# python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "stable-diffusion-v1-5/stable-diffusion-v1-5" --subfolder "vae" --input_image "foo.png" # # python vae_roundtrip.py --use_cuda --pretrained_model_name_or_path "madebyollin/taesd" --use_tiny_nn --input_image "foo.png" # diff --git a/scripts/convert_consistency_decoder.py b/scripts/convert_consistency_decoder.py index 629c784c09..9e28945775 100644 --- a/scripts/convert_consistency_decoder.py +++ b/scripts/convert_consistency_decoder.py @@ -24,7 +24,8 @@ args = args.parse_args() def _extract_into_tensor(arr, timesteps, broadcast_shape): - # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895 """ + # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895 + # """ res = arr[timesteps].float() dims_to_append = len(broadcast_shape) - len(res.shape) return res[(...,) + (None,) * dims_to_append] @@ -507,7 +508,9 @@ def rename_state_dict(sd, embedding): # encode with stable diffusion vae -pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) +pipe = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16 +) pipe.vae.cuda() # construct original decoder with jitted model @@ -1090,7 +1093,7 @@ def new_constructor(self, **kwargs): Encoder.__init__ = new_constructor -vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae") +vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae") consistency_vae = ConsistencyDecoderVAE( encoder_args=vae.encoder.constructor_arguments, decoder_args=unet.config, @@ -1117,7 +1120,7 @@ print((sample_consistency_orig - sample_consistency_new_3).abs().sum()) print("running with diffusers pipeline") pipe = DiffusionPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16 + "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16 ) pipe.to("cuda") diff --git a/src/diffusers/models/auto_model.py b/src/diffusers/models/auto_model.py index a95b0ae64a..947b610ea6 100644 --- a/src/diffusers/models/auto_model.py +++ b/src/diffusers/models/auto_model.py @@ -128,13 +128,13 @@ class AutoModel(ConfigMixin): ```py from diffusers import AutoModel - unet = AutoModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + unet = AutoModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet") ``` If you get the error message below, you need to finetune the weights for your downstream task: ```bash - Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` diff --git a/src/diffusers/models/modeling_flax_utils.py b/src/diffusers/models/modeling_flax_utils.py index fd19578321..3f06099319 100644 --- a/src/diffusers/models/modeling_flax_utils.py +++ b/src/diffusers/models/modeling_flax_utils.py @@ -113,14 +113,14 @@ class FlaxModelMixin(PushToHubMixin): >>> from diffusers import FlaxUNet2DConditionModel >>> # load model - >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/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("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") >>> flat_params = traverse_util.flatten_dict(params) >>> mask = { ... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale")) @@ -149,7 +149,7 @@ class FlaxModelMixin(PushToHubMixin): >>> from diffusers import FlaxUNet2DConditionModel >>> # Download model and configuration from huggingface.co - >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/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) @@ -179,14 +179,14 @@ class FlaxModelMixin(PushToHubMixin): >>> from diffusers import FlaxUNet2DConditionModel >>> # load model - >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/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("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5") >>> flat_params = traverse_util.flatten_dict(params) >>> mask = { ... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale")) @@ -216,8 +216,8 @@ class FlaxModelMixin(PushToHubMixin): pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - - A string, the *model id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained model - hosted on the Hub. + - A string, the *model id* (for example `stable-diffusion-v1-5/stable-diffusion-v1-5`) of a + pretrained model hosted on the Hub. - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved using [`~FlaxModelMixin.save_pretrained`]. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): @@ -271,7 +271,7 @@ class FlaxModelMixin(PushToHubMixin): >>> from diffusers import FlaxUNet2DConditionModel >>> # Download model and configuration from huggingface.co and cache. - >>> model, params = FlaxUNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5") + >>> model, params = FlaxUNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/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/") ``` @@ -279,7 +279,7 @@ class FlaxModelMixin(PushToHubMixin): If you get the error message below, you need to finetune the weights for your downstream task: ```bash - Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py index 1af7ba9ac5..ce26785f63 100644 --- a/src/diffusers/models/modeling_utils.py +++ b/src/diffusers/models/modeling_utils.py @@ -923,13 +923,13 @@ class ModelMixin(torch.nn.Module, PushToHubMixin): ```py from diffusers import UNet2DConditionModel - unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet") ``` If you get the error message below, you need to finetune the weights for your downstream task: ```bash - Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. ``` @@ -1800,7 +1800,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin): ```py from diffusers import UNet2DConditionModel - model_id = "runwayml/stable-diffusion-v1-5" + model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" unet = UNet2DConditionModel.from_pretrained(model_id, subfolder="unet") unet.num_parameters(only_trainable=True) 859520964 diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py index 8a80257473..83e6007e3f 100644 --- a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py +++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py @@ -115,7 +115,7 @@ class StableDiffusionXLInpaintLoopBeforeDenoiser(ModularPipelineBlocks): def check_inputs(components, block_state): num_channels_unet = components.num_channels_unet if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting if block_state.mask is None or block_state.masked_image_latents is None: raise ValueError("mask and masked_image_latents must be provided for inpainting-specific Unet") num_channels_latents = block_state.latents.shape[1] diff --git a/src/diffusers/pipelines/README.md b/src/diffusers/pipelines/README.md index 363caffe20..6f9ab7b291 100644 --- a/src/diffusers/pipelines/README.md +++ b/src/diffusers/pipelines/README.md @@ -159,7 +159,7 @@ init_image = download_image(img_url).resize((512, 512)) mask_image = download_image(mask_url).resize((512, 512)) pipe = StableDiffusionInpaintPipeline.from_pretrained( - "runwayml/stable-diffusion-inpainting", + "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, ) pipe = pipe.to("cuda") diff --git a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py index 1545027a28..3682ddc911 100644 --- a/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py +++ b/src/diffusers/pipelines/controlnet_xs/pipeline_controlnet_xs.py @@ -133,8 +133,8 @@ class StableDiffusionControlNetXSPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py index 003e748274..6f484aa3e2 100644 --- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py +++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py @@ -185,8 +185,8 @@ class AltDiffusionPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -266,8 +266,8 @@ class AltDiffusionPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py index 64bd06d02e..d6bf901207 100644 --- a/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py +++ b/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py @@ -213,8 +213,8 @@ class AltDiffusionImg2ImgPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -294,8 +294,8 @@ class AltDiffusionImg2ImgPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py index 59c79e134e..08f8c7e26f 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_cycle_diffusion.py @@ -162,8 +162,8 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Sta instance of [`DDIMScheduler`]. 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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -226,8 +226,8 @@ class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Sta "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py index 2d9eaa493f..fcd8bf317a 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_onnx_stable_diffusion_inpaint_legacy.py @@ -62,7 +62,8 @@ class OnnxStableDiffusionInpaintPipelineLegacy(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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py index 205ace65ee..ba0dd66c29 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_inpaint_legacy.py @@ -111,7 +111,8 @@ class StableDiffusionInpaintPipelineLegacy( [`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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. """ @@ -196,8 +197,8 @@ class StableDiffusionInpaintPipelineLegacy( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py index d81f0d2625..b7a0be57c1 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_model_editing.py @@ -64,8 +64,8 @@ class StableDiffusionModelEditingPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. with_to_k ([`bool`]): diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py index 2331157ba5..c236e73bf4 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_paradigms.py @@ -46,10 +46,12 @@ EXAMPLE_DOC_STRING = """ >>> from diffusers import DDPMParallelScheduler >>> from diffusers import StableDiffusionParadigmsPipeline - >>> scheduler = DDPMParallelScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler") + >>> scheduler = DDPMParallelScheduler.from_pretrained( + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="scheduler" + ... ) >>> pipe = StableDiffusionParadigmsPipeline.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", scheduler=scheduler, torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") @@ -95,8 +97,8 @@ class StableDiffusionParadigmsPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ diff --git a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py index d000d87e6a..2a461ae20c 100644 --- a/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py +++ b/src/diffusers/pipelines/deprecated/stable_diffusion_variants/pipeline_stable_diffusion_pix2pix_zero.py @@ -303,7 +303,8 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline, StableDiffusionMixin [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`], or [`DDPMScheduler`]. 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/runwayml/stable-diffusion-v1-5) for details. + Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + details. feature_extractor ([`CLIPImageProcessor`]): Model that extracts features from generated images to be used as inputs for the `safety_checker`. requires_safety_checker (bool): diff --git a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py index 61582853b0..9ff8e98577 100644 --- a/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py +++ b/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py @@ -38,8 +38,8 @@ class VersatileDiffusionPipeline(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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py index 1d7733982e..59f733a498 100644 --- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py +++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py @@ -186,8 +186,8 @@ class LatentConsistencyModelImg2ImgPipeline( supports [`LCMScheduler`]. 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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. requires_safety_checker (`bool`, *optional*, defaults to `True`): diff --git a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py index 3e96b44663..e463884618 100644 --- a/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py +++ b/src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py @@ -165,8 +165,8 @@ class LatentConsistencyModelPipeline( supports [`LCMScheduler`]. 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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. requires_safety_checker (`bool`, *optional*, defaults to `True`): diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py index 5b61aaf9b6..fbf4dc23d0 100644 --- a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py +++ b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py @@ -49,7 +49,7 @@ EXAMPLE_DOC_STRING = """ >>> from diffusers.utils import load_image >>> pipe = LEditsPPPipelineStableDiffusion.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe.enable_vae_tiling() >>> pipe = pipe.to("cuda") @@ -381,8 +381,8 @@ class LEditsPPPipelineStableDiffusion( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py index de66871922..1abef01430 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd.py @@ -80,7 +80,10 @@ EXAMPLE_DOC_STRING = """ >>> # load control net and stable diffusion v1-5 >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) >>> pipe = AutoPipelineForText2Image.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", + ... controlnet=controlnet, + ... torch_dtype=torch.float16, + ... enable_pag=True, ... ) >>> # speed up diffusion process with faster scheduler and memory optimization @@ -202,8 +205,8 @@ class StableDiffusionControlNetPAGPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ diff --git a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py index 3daaac328c..2781af7890 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_inpaint.py @@ -93,7 +93,10 @@ EXAMPLE_DOC_STRING = """ ... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16 ... ) >>> pipe = AutoPipelineForInpainting.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, enable_pag=True + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", + ... controlnet=controlnet, + ... torch_dtype=torch.float16, + ... enable_pag=True, ... ) >>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) @@ -151,10 +154,11 @@ class StableDiffusionControlNetPAGInpaintPipeline( - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters > [!TIP] > This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting > - ([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as > - default text-to-image Stable Diffusion checkpoints > - ([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image > - Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as > + ([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)) + as well as > default text-to-image Stable Diffusion checkpoints > + ([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)). + Default text-to-image > Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned + on those, such as > [lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint). Args: @@ -175,8 +179,8 @@ class StableDiffusionControlNetPAGInpaintPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -1328,7 +1332,7 @@ class StableDiffusionControlNetPAGInpaintPipeline( # 7.1 Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd.py b/src/diffusers/pipelines/pag/pipeline_pag_sd.py index 349d006aad..ea64f8be2c 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_sd.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_sd.py @@ -57,7 +57,7 @@ EXAMPLE_DOC_STRING = """ >>> from diffusers import AutoPipelineForText2Image >>> pipe = AutoPipelineForText2Image.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True ... ) >>> pipe = pipe.to("cuda") @@ -190,8 +190,8 @@ class StableDiffusionPAGPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -272,8 +272,8 @@ class StableDiffusionPAGPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py index e9a846b5e2..8351112ce4 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_img2img.py @@ -61,7 +61,7 @@ EXAMPLE_DOC_STRING = """ >>> from diffusers.utils import load_image >>> pipe = AutoPipelineForImage2Image.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", ... torch_dtype=torch.float16, ... enable_pag=True, ... ) @@ -185,8 +185,8 @@ class StableDiffusionPAGImg2ImgPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -267,8 +267,8 @@ class StableDiffusionPAGImg2ImgPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py index ee9d20f368..6b1b294e10 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_inpaint.py @@ -58,7 +58,7 @@ EXAMPLE_DOC_STRING = """ >>> from diffusers import AutoPipelineForInpainting >>> pipe = AutoPipelineForInpainting.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16, enable_pag=True ... ) >>> pipe = pipe.to("cuda") >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" @@ -217,8 +217,8 @@ class StableDiffusionPAGInpaintPipeline( [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -299,8 +299,8 @@ class StableDiffusionPAGInpaintPipeline( "The configuration file of the unet has set the default `sample_size` to smaller than" " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" - " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" - " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" + " \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" " in the config might lead to incorrect results in future versions. If you have downloaded this" " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" @@ -1183,7 +1183,7 @@ class StableDiffusionPAGInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py index 2e12a4a97f..2a8f7a448d 100644 --- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py +++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py @@ -1501,7 +1501,7 @@ class StableDiffusionXLPAGInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py index 61435b80ca..c09992befb 100644 --- a/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py +++ b/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py @@ -179,8 +179,8 @@ class PaintByExamplePipeline(DeprecatedPipelineMixin, DiffusionPipeline, StableD [`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/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. diff --git a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py index a5f67bffe6..49b09e205c 100644 --- a/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py +++ b/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py @@ -48,8 +48,8 @@ class SemanticStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`Q16SafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. - Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details - about a model's potential harms. + Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for + more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ @@ -332,7 +332,7 @@ class SemanticStableDiffusionPipeline(DeprecatedPipelineMixin, DiffusionPipeline >>> from diffusers import SemanticStableDiffusionPipeline >>> pipe = SemanticStableDiffusionPipeline.from_pretrained( - ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 + ... "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") diff --git a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py index bed596e57c..5b2cca0378 100644 --- a/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py @@ -1247,7 +1247,7 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro # match the inpainting pipeline and will be updated with input + mask inpainting model later if num_channels_transformer == 33: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if ( diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py index 18f8536a75..88cc7515b0 100644 --- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py @@ -1501,7 +1501,7 @@ class StableDiffusionXLInpaintPipeline( # 8. Check that sizes of mask, masked image and latents match if num_channels_unet == 9: - # default case for runwayml/stable-diffusion-inpainting + # default case for stable-diffusion-v1-5/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels: diff --git a/src/diffusers/utils/hub_utils.py b/src/diffusers/utils/hub_utils.py index b6e99452aa..d0b05c7d95 100644 --- a/src/diffusers/utils/hub_utils.py +++ b/src/diffusers/utils/hub_utils.py @@ -113,7 +113,8 @@ def load_or_create_model_card( Args: repo_id_or_path (`str`): - The repo id (e.g., "runwayml/stable-diffusion-v1-5") or local path where to look for the model card. + The repo id (e.g., "stable-diffusion-v1-5/stable-diffusion-v1-5") or local path where to look for the model + card. token (`str`, *optional*): Authentication token. Will default to the stored token. See https://huggingface.co/settings/token for more details. diff --git a/tests/pipelines/pag/test_pag_sd_inpaint.py b/tests/pipelines/pag/test_pag_sd_inpaint.py index 709df68370..754158bbf1 100644 --- a/tests/pipelines/pag/test_pag_sd_inpaint.py +++ b/tests/pipelines/pag/test_pag_sd_inpaint.py @@ -255,7 +255,7 @@ class StableDiffusionPAGInpaintPipelineFastTests( @require_torch_accelerator class StableDiffusionPAGPipelineIntegrationTests(unittest.TestCase): pipeline_class = StableDiffusionPAGInpaintPipeline - repo_id = "runwayml/stable-diffusion-v1-5" + repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" def setUp(self): super().setUp()