Update README.md

README.md

@@ -86,6 +86,44 @@ image = pipe(
 ).images[0]
 ```
 
+# Multi-Inference
+```python
+import torch
+from diffusers.utils import load_image
+
+# https://github.com/huggingface/diffusers/pull/11350, after merging, you can directly import from diffusers
+# from diffusers import FluxControlNetPipeline, FluxControlNetModel
+
+# use local files for this moment
+from pipeline_flux_controlnet import FluxControlNetPipeline
+from controlnet_flux import FluxControlNetModel
+
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0'
+
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
+pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=[controlnet], torch_dtype=torch.bfloat16) # use [] to enable multi-CNs
+pipe.to("cuda")
+
+# replace with other conds
+control_image = load_image("./conds/canny.png")
+width, height = control_image.size
+
+prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."
+
+image = pipe(
+    prompt, 
+    control_image=[control_image, control_image], # try with different conds such as canny&depth, pose&depth
+    width=width,
+    height=height,
+    controlnet_conditioning_scale=[0.35, 0.35],
+    control_guidance_end=[0.8, 0.8],
+    num_inference_steps=30, 
+    guidance_scale=3.5,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+).images[0]
+```
+
 # Recommended Parameters
 You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we suggest to use multi-conditions.
 - Canny: use cv2.Canny, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.