Update README.md

README.md

@@ -34,6 +34,36 @@ RCNA MINI is based on the LoRA architecture, which fine-tunes diffusion models u
 - **Architecture**: LoRA applied to diffusion models
 - **Inference Steps**: 4-step generation
 - **Output Length**: 4 to 16 seconds
+- 
+## Using AnimateLCM with Diffusers
 
+```python
+import torch
+from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter, DiffusionPipeline
+from diffusers.utils import export_to_gif
+
+# Load AnimateLCM for video generation
+adapter = MotionAdapter.from_pretrained("Binarybardakshat/RCNA_MINI")
+pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter, torch_dtype=torch.float16)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
+pipe.load_lora_weights("Binarybardakshat/RCNA_MINI", weight_name="RCNA_LORA_MINI_1.safetensors", adapter_name="lcm-lora")
+pipe.set_adapters(["lcm-lora"], [0.8])
+pipe.enable_vae_slicing()
+pipe.enable_model_cpu_offload()
+
+# Generate video using RCNA MINI
+output = pipe(
+    prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution",
+    negative_prompt="bad quality, worse quality, low resolution",
+    num_frames=16,
+    guidance_scale=2.0,
+    num_inference_steps=6,
+    generator=torch.Generator("cpu").manual_seed(0),
+)
+frames = output.frames[0]
+export_to_gif(frames, "animatelcm.gif")
+print("Video and image generation complete!")
+
+```
 ## License:
 This model is licensed under the [MIT License](LICENSE).