Remove code snippet (#2)

- Remove code snippet (2534fe6a7ab575362434b688266de774be5566c3)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -3,7 +3,6 @@ datasets:
 - michaelyuanqwq/roboseg
 license: mit
 pipeline_tag: image-to-image
-library_name: diffusers
 ---
 
 <h1> RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation </h1>
@@ -18,55 +17,7 @@ Please checkout https://github.com/michaelyuanqwq/roboengine for more details.
 
 ## Usage
 
-This model is a ControlNet model compatible with the `diffusers` library, specifically designed for background generation in robot scenes. It works by taking an image (e.g., a robot on a black background, created using a segmentation mask) as conditioning and generating a new background based on a text prompt.
-
-```python
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
-from PIL import Image
-import torch
-
-# Load the ControlNet model
-controlnet = ControlNetModel.from_pretrained("michaelyuanqwq/roboengine-bg-diffusion", torch_dtype=torch.float16)
-
-# Load a base Stable Diffusion XL pipeline (this ControlNet is designed for SDXL)
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
-)
-pipe.to("cuda")
-
-# Prepare your input image and segmentation mask
-# In a real application, these would come from your dataset or a segmentation model.
-# The 'michaelyuanqwq/roboseg' dataset can provide examples.
-# For demonstration: create a dummy input image and a white mask for the robot
-input_image = Image.new("RGB", (768, 768), color = 'red') # Placeholder for your actual robot image
-mask = Image.new("L", (768, 768), color = 'black') # Placeholder for your actual robot mask (white for robot, black for background)
-from PIL import ImageDraw
-draw = ImageDraw.Draw(mask)
-draw.ellipse((100, 100, 668, 668), fill='white') # Draw a white circle as a dummy robot mask
-
-# Create the conditioning image for ControlNet: robot on a black background
-# This image tells ControlNet to preserve the white areas (robot) and generate new content for the black areas (background).
-control_image = Image.composite(Image.new("RGB", input_image.size, (0, 0, 0)), input_image, mask.convert("1"))
-
-# Define your text prompt for the new background
-prompt = "A robot arm working in a futuristic lab with neon lights, high detail, photorealistic"
-negative_prompt = "blurry, low quality, bad anatomy, deformed"
-
-# Generate the image
-generator = torch.Generator(device="cuda").manual_seed(42) # For reproducible results
-output_image = pipe(
-    prompt=prompt,
-    image=control_image, # The conditioning image derived from the mask
-    negative_prompt=negative_prompt,
-    num_inference_steps=25,
-    generator=generator,
-    guidance_scale=7.5,
-).images[0]
-
-# Save the generated image
-output_image.save("generated_robot_scene.png")
-print("Generated image saved as generated_robot_scene.png")
-```
+See the Github repo: https://github.com/michaelyuanqwq/roboengine.
 
 ### BibTex
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