Update README.md

README.md

@@ -49,14 +49,55 @@ pipe_t2i.enable_lora()
 out = pipe_t2i(prompt=prompt, guidance_scale=3.5, height=768, width=512, num_inference_steps=50).images[0]
 out.save("t2i_color_palette.png")
 ```
+## Conditional Generation
+<table>
+  <tr>
+    <td><img src="./pictures/blue.png" style="width:100%;"></td>
+    <td><img src="./pictures/red.png" style="width:100%;"></td>
+    <td><img src="./pictures/yellow.png" style="width:100%;"></td>
+  </tr>
+  <tr>
+    <td><img src="./pictures/inpainting_bottle_blue.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_bottle_red.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_bottle_yellow.png" style="width:100%;"></td>
+  </tr>
+  <tr>
+    <td><img src="./pictures/inpainting_bag_blue.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_bag_red.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_bag_yellow.png" style="width:100%;"></td>
+  </tr>
+  <tr>
+    <td><img src="./pictures/inpainting_backpack_blue.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_backpack_red.png" style="width:100%;"></td>
+    <td><img src="./pictures/inpainting_backpack_yellow.png" style="width:100%;"></td>
+  </tr>
+</table>
 
-# Model description
-
+```python
+import torch
+import numpy as np
+from PIL import Image
+from diffusers.utils import load_image
+from diffusers import FluxInpaintPipeline
 
+pipe_inpainting = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cuda")
+pipe_inpainting.load_lora_weights("./color_palette_lora.safetensors")
+mask = load_image("./mask.jpg").resize(size=(768, 512))
+color_palette = load_image("./blue.png")
+input_image = Image.new('RGB', (768, 512))
+input_image.paste(color_palette.resize(size=(256, 512)), (0, 256))
+input_image.paste(color_palette.resize(size=(512, 512)), (512, 256))
+out = pipe_inpainting(prompt=prompt, image=color_palette, mask_image=mask, guidance_scale=3.5, height=768, width=512, num_inference_steps=50, max_sequence_length=256, strength=1).images[0]
+out.save("conda_color_palette.png")
+```
 
-# Inference
 
+# Model description
+The model follows the idea of IC-Lora,  image splicing is used for training and inferencing. The IC-Lora prompt template is like this👇
 
+```
+[COLOR_PALETTE] This two-part image showcases the transformation from a color palette to a image. [LEFT] a color palette with eight 5 different colors.  [RIGHT] xxxxx
+```
 
 # Training