docs: cleanup of runway model (#12503)
* cleanup of runway model * quality fixes
This commit is contained in:
Ali Imran
@@ -39,7 +39,7 @@ mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images
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original_image = load_image(img_url).resize((512, 512))
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mask_image = load_image(mask_url).resize((512, 512))
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pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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pipe = StableDiffusionInpaintPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
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pipe.vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
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pipe.to("cuda")
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@@ -21,7 +21,7 @@ The Stable Diffusion model can also infer depth based on an image using [MiDaS](
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## StableDiffusionDepth2ImgPipeline
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@@ -21,14 +21,14 @@ The Stable Diffusion model can also be applied to inpainting which lets you edit
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## Tips
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It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
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as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
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as [stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting). Default
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text-to-image Stable Diffusion checkpoints, such as
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[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.
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## StableDiffusionInpaintPipeline
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@@ -17,7 +17,7 @@ The Stable Diffusion latent upscaler model was created by [Katherine Crowson](ht
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## StableDiffusionLatentUpscalePipeline
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@@ -22,7 +22,7 @@ Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B data
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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.
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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!
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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!
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The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo:
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@@ -64,7 +64,7 @@ The table below summarizes the available Stable Diffusion pipelines, their suppo
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<a href="./inpaint">StableDiffusionInpaint</a>
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</td>
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<td class="px-4 py-2 text-gray-700">inpainting</td>
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<td class="px-4 py-2"><a href="https://huggingface.co/spaces/runwayml/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
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<td class="px-4 py-2"><a href="https://huggingface.co/spaces/stable-diffusion-v1-5/stable-diffusion-inpainting"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a>
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</td>
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</tr>
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<tr>
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@@ -36,7 +36,7 @@ Here are some examples for how to use Stable Diffusion 2 for each task:
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## Text-to-image
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@@ -25,7 +25,7 @@ The abstract from the paper is:
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## StableDiffusionPipeline
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@@ -21,7 +21,7 @@ The Stable Diffusion upscaler diffusion model was created by the researchers and
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> [!TIP]
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> 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!
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>
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> 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!
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> 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!
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## StableDiffusionUpscalePipeline
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@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
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4
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```
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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):
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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):
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```py
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from diffusers import StableDiffusionPipeline
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pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
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pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting", use_safetensors=True)
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pipeline.unet.config["in_channels"]
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9
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```
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@@ -215,7 +215,7 @@ from diffusers import AutoPipelineForInpainting, LCMScheduler
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from diffusers.utils import load_image, make_image_grid
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pipe = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting",
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"stable-diffusion-v1-5/stable-diffusion-inpainting",
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torch_dtype=torch.float16,
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variant="fp16",
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).to("cuda")
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@@ -112,7 +112,7 @@ blurred_mask
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## Popular models
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[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.
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[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.
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### Stable Diffusion Inpainting
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@@ -124,7 +124,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -244,7 +244,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
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```
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</hfoption>
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<hfoption id="runwayml/stable-diffusion-inpainting">
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<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpainting">
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```py
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import torch
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@@ -252,7 +252,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -278,7 +278,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
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</div>
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<div>
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<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint-specific.png"/>
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<figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
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<figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
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</div>
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</div>
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@@ -308,7 +308,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
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```
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</hfoption>
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<hfoption id="runwayml/stable-diffusion-inpaint">
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<hfoption id="stable-diffusion-v1-5/stable-diffusion-inpaint">
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```py
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import torch
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@@ -316,7 +316,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -340,7 +340,7 @@ make_image_grid([init_image, image], rows=1, cols=2)
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</div>
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<div>
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<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/specific-inpaint-basic.png"/>
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<figcaption class="mt-2 text-center text-sm text-gray-500">runwayml/stable-diffusion-inpainting</figcaption>
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<figcaption class="mt-2 text-center text-sm text-gray-500">stable-diffusion-v1-5/stable-diffusion-inpainting</figcaption>
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</div>
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</div>
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@@ -358,7 +358,7 @@ from diffusers.utils import load_image, make_image_grid
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device = "cuda"
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting",
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"stable-diffusion-v1-5/stable-diffusion-inpainting",
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torch_dtype=torch.float16,
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variant="fp16"
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)
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@@ -396,7 +396,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -441,7 +441,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -481,7 +481,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -606,7 +606,7 @@ from diffusers import AutoPipelineForInpainting, AutoPipelineForImage2Image
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from diffusers.utils import load_image, make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -683,7 +683,7 @@ from diffusers import AutoPipelineForInpainting
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from diffusers.utils import make_image_grid
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pipeline = AutoPipelineForInpainting.from_pretrained(
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"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16,
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"stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16,
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -714,7 +714,7 @@ controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpai
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# pass ControlNet to the pipeline
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pipeline = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
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"stable-diffusion-v1-5/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, variant="fp16"
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)
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pipeline.enable_model_cpu_offload()
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# remove following line if xFormers is not installed or you have PyTorch 2.0 or higher installed
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@@ -173,7 +173,7 @@ mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data
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init_image = download_image(img_url).resize((512, 512))
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mask_image = download_image(mask_url).resize((512, 512))
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path = "runwayml/stable-diffusion-inpainting"
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path = "stable-diffusion-v1-5/stable-diffusion-inpainting"
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run_compile = True # Set True / False
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@@ -28,12 +28,12 @@ pipeline.unet.config["in_channels"]
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4
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```
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인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
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인페인팅은 입력 샘플에 9개의 채널이 필요합니다. [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:
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```py
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from diffusers import StableDiffusionPipeline
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pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
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pipeline = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-inpainting")
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pipeline.unet.config["in_channels"]
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9
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```
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@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
|
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[[open-in-colab]]
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|
||||
[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
|
||||
[`StableDiffusionInpaintPipeline`]은 마스크와 텍스트 프롬프트를 제공하여 이미지의 특정 부분을 편집할 수 있도록 합니다. 이 기능은 인페인팅 작업을 위해 특별히 훈련된 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)과 같은 Stable Diffusion 버전을 사용합니다.
|
||||
|
||||
먼저 [`StableDiffusionInpaintPipeline`] 인스턴스를 불러옵니다:
|
||||
|
||||
@@ -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]
|
||||
> 이전의 실험적인 인페인팅 구현에서는 품질이 낮은 다른 프로세스를 사용했습니다. 이전 버전과의 호환성을 보장하기 위해 새 모델이 포함되지 않은 사전학습된 파이프라인을 불러오면 이전 인페인팅 방법이 계속 적용됩니다.
|
||||
|
||||
아래 Space에서 이미지 인페인팅을 직접 해보세요!
|
||||
|
||||
<iframe
|
||||
src="https://runwayml-stable-diffusion-inpainting.hf.space"
|
||||
frameborder="0"
|
||||
width="850"
|
||||
height="500"
|
||||
></iframe>
|
||||
|
||||
@@ -16,12 +16,12 @@ pipeline.unet.config["in_channels"]
|
||||
4
|
||||
```
|
||||
|
||||
而图像修复任务需要输入样本具有9个通道。您可以在 [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
|
||||
而图像修复任务需要输入样本具有9个通道。您可以在 [`stable-diffusion-v1-5/stable-diffusion-inpainting`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting) 这样的预训练修复模型中验证此参数：
|
||||
|
||||
```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
|
||||
```
|
||||
|
||||
Reference in New Issue
Block a user