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Parent(s):
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add diffusers weights (#1)
Browse files- add weights (678355f2e2e45ab5a9413799f386ead2bff81aef)
- add readme (ce200773d085a919b55deed0eceaa12b1c4fd75a)
- update the readme (2c24837a61b85ab7ac2da90d0a9bc29323979dc8)
- up (1c503edc64ca10e37cd36a8f145961e1fd34e119)
Co-authored-by: YiYi Xu <YiYiXu@users.noreply.huggingface.co>
- README.md +39 -0
- config.json +21 -0
- diffusion_pytorch_model.safetensors +3 -0
README.md
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@@ -91,6 +91,45 @@ Which should give you an image like below:
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![A girl sitting in a cafe](sample_result.png)
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### Preprocessing
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![A girl sitting in a cafe](sample_result.png)
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### Using Controlnets in Diffusers
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Make sure you upgrade to the latest diffusers version: `pip install -U diffusers`. And then you can run:
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```python
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import torch
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from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel
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from diffusers.utils import load_image
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controlnet = SD3ControlNetModel.from_pretrained("stabilityai/stable-diffusion-3.5-large-controlnet-depth", torch_dtype=torch.float16)
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pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-3.5-large",
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controlnet=controlnet,
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torch_dtype=torch.float16,
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).to("cuda")
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control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png")
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generator = torch.Generator(device="cpu").manual_seed(0)
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image = pipe(
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prompt = "a photo of a man",
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control_image=control_image,
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guidance_scale=4.5,
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num_inference_steps=40,
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generator=generator,
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max_sequence_length=77,
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).images[0]
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image.save('depth-8b.jpg')
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```
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You can use `image_gen_aux` to extract `depth_image`, which contains all the preprocessor required to use with diffusers pipelines.
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```python
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# install image_gen_aux with: pip install git+https://github.com/huggingface/image_gen_aux.git
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from image_gen_aux import DepthPreprocessor
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image = load_image("path to image")
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depth_preprocessor = DepthPreprocessor.from_pretrained("depth-anything/Depth-Anything-V2-Large-hf").to("cuda")
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depth_image = depth_preprocessor(image, invert=True)[0].convert("RGB")
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```
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### Preprocessing
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config.json
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{
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"_class_name": "SD3ControlNetModel",
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"_diffusers_version": "0.32.0.dev0",
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"attention_head_dim": 64,
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"caption_projection_dim": 2048,
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"dual_attention_layers": [],
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"extra_conditioning_channels": 0,
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"force_zeros_for_pooled_projection": false,
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"in_channels": 16,
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"joint_attention_dim": null,
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"num_attention_heads": 38,
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"num_layers": 19,
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"out_channels": 16,
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"patch_size": 2,
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"pooled_projection_dim": 2048,
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"pos_embed_max_size": null,
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"pos_embed_type": null,
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"qk_norm": null,
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"sample_size": 128,
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"use_pos_embed": false
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}
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diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5cd54d6c670ebc864b459f251573bc0f9cb60f2adf644dbd4df4ffab6879b1a1
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size 8614110992
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