Update README.md

README.md

@@ -1,43 +1,44 @@
----
-license: apache-2.0
----
-
-# SD3-ControlNet-Depth
-
-<img src="./assets/teaser.png"/>
-
-# Demo
-```python
-import torch
-from diffusers import StableDiffusion3ControlNetPipeline
-from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
-from diffusers.utils import load_image
-
-# load pipeline
-controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Depth")
-pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3-medium-diffusers",
-    controlnet=controlnet
-)
-pipe.to("cuda", torch.float16)
-
-# config
-control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Depth/resolve/main/images/depth.jpeg")
-prompt = "a panda cub, captured in a close-up, in forest, is perched on a tree trunk. good composition, Photography, the cub's ears, a fluffy black, are tucked behind its head, adding a touch of whimsy to its appearance. a lush tapestry of green leaves in the background. depth of field, National Geographic"
-n_prompt = "bad hands, blurry, NSFW, nude, naked, porn, ugly, bad quality, worst quality"
-
-# to reproduce result in our example
-generator = torch.Generator(device="cpu").manual_seed(4000)
-image = pipe(
-    prompt, 
-    negative_prompt=n_prompt, 
-    control_image=control_image, 
-    controlnet_conditioning_scale=0.5,
-    guidance_scale=7.0,
-    generator=generator
-).images[0]
-image.save('image.jpg')
-```
-
-# Limitation
-Due to the fact that only 1024*1024 pixel resolution was used during the training phase, the inference performs best at this size, with other sizes yielding suboptimal results.
+---
+license: apache-2.0
+library_name: diffusers
+---
+
+# SD3-ControlNet-Depth
+
+<img src="./assets/teaser.png"/>
+
+# Demo
+```python
+import torch
+from diffusers import StableDiffusion3ControlNetPipeline
+from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
+from diffusers.utils import load_image
+
+# load pipeline
+controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Depth")
+pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-3-medium-diffusers",
+    controlnet=controlnet
+)
+pipe.to("cuda", torch.float16)
+
+# config
+control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Depth/resolve/main/images/depth.jpeg")
+prompt = "a panda cub, captured in a close-up, in forest, is perched on a tree trunk. good composition, Photography, the cub's ears, a fluffy black, are tucked behind its head, adding a touch of whimsy to its appearance. a lush tapestry of green leaves in the background. depth of field, National Geographic"
+n_prompt = "bad hands, blurry, NSFW, nude, naked, porn, ugly, bad quality, worst quality"
+
+# to reproduce result in our example
+generator = torch.Generator(device="cpu").manual_seed(4000)
+image = pipe(
+    prompt, 
+    negative_prompt=n_prompt, 
+    control_image=control_image, 
+    controlnet_conditioning_scale=0.5,
+    guidance_scale=7.0,
+    generator=generator
+).images[0]
+image.save('image.jpg')
+```
+
+# Limitation
+Due to the fact that only 1024*1024 pixel resolution was used during the training phase, the inference performs best at this size, with other sizes yielding suboptimal results.