Instructions to use jwengr/stable-diffusion-2-gray-inpaint-to-rgb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jwengr/stable-diffusion-2-gray-inpaint-to-rgb with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jwengr/stable-diffusion-2-gray-inpaint-to-rgb", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
gray-inpaint/config.json
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@@ -10,6 +10,6 @@
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"height": 512,
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"model_type": "sd_gray_inpaint",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"width": 512
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}
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"height": 512,
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"model_type": "sd_gray_inpaint",
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"torch_dtype": "float32",
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"transformers_version": "4.47.0",
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"width": 512
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}
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gray-inpaint/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 4055354432
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d7e04ec5bbbda567c7fa7e1d8478f997ec5bc1376dc3fa633dc737171b774da
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size 4055354432
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gray-inpaint/modeling_sd_gray_inpaint.py
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@@ -64,6 +64,7 @@ class SDGrayInpaintModel(PreTrainedModel):
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masks_logits = self.mask_predictor(images_gray_masked)
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masks = (torch.sigmoid(masks_logits)>0.5)*1.
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masks = masks.float().to(self.vae.device)
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B, C, H, W = images_gray_masked.shape
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prompt_embeds = self.prompt_embeds.repeat(B,1,1)
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masks_logits = self.mask_predictor(images_gray_masked)
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masks = (torch.sigmoid(masks_logits)>0.5)*1.
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masks = masks.float().to(self.vae.device)
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images_gray_masked = (1-masks) * images_gray_masked
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B, C, H, W = images_gray_masked.shape
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prompt_embeds = self.prompt_embeds.repeat(B,1,1)
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modeling_sd_gray_inpaint.py
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@@ -64,6 +64,7 @@ class SDGrayInpaintModel(PreTrainedModel):
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masks_logits = self.mask_predictor(images_gray_masked)
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masks = (torch.sigmoid(masks_logits)>0.5)*1.
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masks = masks.float().to(self.vae.device)
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B, C, H, W = images_gray_masked.shape
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prompt_embeds = self.prompt_embeds.repeat(B,1,1)
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masks_logits = self.mask_predictor(images_gray_masked)
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masks = (torch.sigmoid(masks_logits)>0.5)*1.
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masks = masks.float().to(self.vae.device)
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images_gray_masked = (1-masks) * images_gray_masked
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B, C, H, W = images_gray_masked.shape
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prompt_embeds = self.prompt_embeds.repeat(B,1,1)
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