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--- |
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license: openrail++ |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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tags: |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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- text-to-image |
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- diffusers |
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- controlnet |
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inference: false |
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--- |
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# SDXL-controlnet: Depth |
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These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. You can find some example images in the following. |
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prompt: spiderman lecture, photorealistic |
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## Usage |
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Make sure to first install the libraries: |
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```bash |
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pip install accelerate transformers safetensors diffusers |
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``` |
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And then we're ready to go: |
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```python |
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import torch |
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import numpy as np |
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from PIL import Image |
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from transformers import DPTFeatureExtractor, DPTForDepthEstimation |
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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL |
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from diffusers.utils import load_image |
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depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda") |
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feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas") |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-depth-sdxl-1.0", |
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variant="fp16", |
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use_safetensors=True, |
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torch_dtype=torch.float16, |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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controlnet=controlnet, |
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vae=vae, |
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variant="fp16", |
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use_safetensors=True, |
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torch_dtype=torch.float16, |
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) |
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pipe.enable_model_cpu_offload() |
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def get_depth_map(image): |
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image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda") |
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with torch.no_grad(), torch.autocast("cuda"): |
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depth_map = depth_estimator(image).predicted_depth |
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depth_map = torch.nn.functional.interpolate( |
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depth_map.unsqueeze(1), |
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size=(1024, 1024), |
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mode="bicubic", |
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align_corners=False, |
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) |
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depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True) |
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depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True) |
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depth_map = (depth_map - depth_min) / (depth_max - depth_min) |
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image = torch.cat([depth_map] * 3, dim=1) |
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image = image.permute(0, 2, 3, 1).cpu().numpy()[0] |
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image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8)) |
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return image |
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prompt = "stormtrooper lecture, photorealistic" |
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image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png") |
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controlnet_conditioning_scale = 0.5 # recommended for good generalization |
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depth_image = get_depth_map(image) |
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images = pipe( |
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prompt, image=depth_image, num_inference_steps=30, controlnet_conditioning_scale=controlnet_conditioning_scale, |
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).images |
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images[0] |
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images[0].save(f"stormtrooper.png") |
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``` |
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For more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl). |
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### Training |
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Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md). |
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#### Training data and Compute |
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The model is trained on 3M image-text pairs from LAION-Aesthetics V2. The model is trained for 700 GPU hours on 80GB A100 GPUs. |
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#### Batch size |
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Data parallel with a single GPU batch size of 8 for a total batch size of 256. |
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#### Hyper Parameters |
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The constant learning rate of 1e-5. |
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#### Mixed precision |
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fp16 |
