Create README.md

README.md

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+
+---
+license: openrail++
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+- text-to-image
+- diffusers
+- controlnet
+inference: false
+---
+    
+# SDXL-controlnet: Depth
+
+These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with depth conditioning. This checkpoint is 7x smaller than the original XL controlnet checkpoint. You can find some example images in the following.
+
+prompt: spiderman lecture, photorealistic
+![images_0)](./spiderman_small.png)
+
+prompt: aerial view, a futuristic research complex in a bright foggy jungle, hard lighting
+![images_1)](./hf_logo_small.png)
+
+prompt: megatron in an apocalyptic world ground, runied city in the background, photorealistic
+![images_2)](./megatron_small.png)
+
+## Usage
+
+Make sure to first install the libraries:
+
+```bash
+pip install accelerate transformers safetensors diffusers
+```
+
+And then we're ready to go:
+
+```python
+import torch
+import numpy as np
+from PIL import Image
+
+from transformers import DPTFeatureExtractor, DPTForDepthEstimation
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+
+
+depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
+feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+controlnet = ControlNetModel.from_pretrained(
+    "diffusers/controlnet-depth-sdxl-1.0-small",
+    variant="fp16",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+).to("cuda")
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    variant="fp16",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+).to("cuda")
+pipe.enable_model_cpu_offload()
+
+def get_depth_map(image):
+    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
+    with torch.no_grad(), torch.autocast("cuda"):
+        depth_map = depth_estimator(image).predicted_depth
+
+    depth_map = torch.nn.functional.interpolate(
+        depth_map.unsqueeze(1),
+        size=(1024, 1024),
+        mode="bicubic",
+        align_corners=False,
+    )
+    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+    image = torch.cat([depth_map] * 3, dim=1)
+
+    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+    return image
+
+
+prompt = "stormtrooper lecture, photorealistic"
+image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-depth/resolve/main/images/stormtrooper.png")
+controlnet_conditioning_scale = 0.5  # recommended for good generalization
+
+depth_image = get_depth_map(image)
+
+images = pipe(
+    prompt, image=depth_image, num_inference_steps=30, controlnet_conditioning_scale=controlnet_conditioning_scale,
+).images
+images[0]
+
+images[0].save(f"stormtrooper_grid.png")
+```
+
+![]("stormtrooper_grid.png")
+
+To more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl).
+
+🚨 Please note that this checkpoint is experimental and there's a lot of room for improvement. We encourage the community to build on top of it, improve it, and provide us with feedback. 🚨
+
+### Training
+
+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). 
+You can refer to [this script](https://github.com/huggingface/diffusers/blob/7b93c2a882d8e12209fbaeffa51ee2b599ab5349/examples/research_projects/controlnet/train_controlnet_webdataset.py) for full discolsure.
+
+* This checkpoint does not perform distillation. We just use a smaller ControlNet initialized from the SDXL UNet. We
+encourage the community to try and conduct distillation too. This resource might be of help in [this regard](https://huggingface.co/blog/sd_distillation). 
+* To learn more about how the ControlNet was initialized, refer to [this code block](https://github.com/huggingface/diffusers/blob/7b93c2a882d8e12209fbaeffa51ee2b599ab5349/examples/research_projects/controlnet/train_controlnet_webdataset.py#L981C1-L999C36). 
+* It does not have any attention blocks.
+* The model works pretty good on most conditioning images. But for more complex conditionings, the bigger checkpoints might be better. We are still working on improving the quality of this checkpoint and looking for feedback from the community.
+* We recommend playing around with the `controlnet_conditioning_scale` and `guidance_scale` arguments for potentially better
+image generation quality.
+
+#### Training data
+The model was trained on 3M images from LAION aesthetic 6 plus subset, with batch size of 256 for 50k steps with constant learning rate of 3e-5.
+
+#### Compute
+One 8xA100 machine
+
+#### Mixed precision
+FP16