Update README.md

README.md

@@ -23,17 +23,28 @@ Target-Driven Distillation: Consistency Distillation with Target Timestep Select
   Samples generated by TDD-distilled SDXL, with only 4--8 steps.
 </div>
 
-## Update
-[2024.09.20]:Upload the TDD LoRA weights of FLUX-TDD-BETA(4-8-steps) 
-[2024.08.25]:Upload the TDD LoRA weights of SVD
-[2024.08.22]:Upload the TDD LoRA weights of Stable Diffusion XL, YamerMIX and RealVisXL-V4.0, fast text-to-image generation.
-- sdxl_tdd_lora_weights.safetensors
-- yamermix_tdd_lora_weights.safetensors
-- realvis_tdd_sdxl_lora_weights.safetensors
-
-Thanks to [Yamer](https://civitai.com/user/Yamer) and [SG_161222](https://civitai.com/user/SG_161222) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=395107) and [RealVisXL V4.0](https://civitai.com/models/139562/realvisxl-v40) respectively.
-## Usage
+## Usage FLUX
+```python
+from huggingface_hub import hf_hub_download
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights(hf_hub_download("RED-AIGC/TDD", "TDD-FLUX.1-dev-lora-beta.safetensors"))
+pipe.fuse_lora(lora_scale=0.125)
+pipe.to("cuda")
+
+image_flux = pipe(
+    prompt=[prompt],
+    generator=torch.Generator().manual_seed(int(3413)),
+    num_inference_steps=8,
+    guidance_scale=2.0,
+    height=1024,
+    width=1024,
+    max_sequence_length=256
+).images[0]
+```
 
+## Usage SDXL
 You can directly download the model in this repository.
 You also can download the model in python script:
 
@@ -71,6 +82,17 @@ image = pipe(
 image.save("tdd.png")
 ```
 
+## Update
+[2024.09.20]:Upload the TDD LoRA weights of FLUX-TDD-BETA(4-8-steps) 
+[2024.08.25]:Upload the TDD LoRA weights of SVD
+[2024.08.22]:Upload the TDD LoRA weights of Stable Diffusion XL, YamerMIX and RealVisXL-V4.0, fast text-to-image generation.
+- sdxl_tdd_lora_weights.safetensors
+- yamermix_tdd_lora_weights.safetensors
+- realvis_tdd_sdxl_lora_weights.safetensors
+
+Thanks to [Yamer](https://civitai.com/user/Yamer) and [SG_161222](https://civitai.com/user/SG_161222) for developing [YamerMIX](https://civitai.com/models/84040?modelVersionId=395107) and [RealVisXL V4.0](https://civitai.com/models/139562/realvisxl-v40) respectively.
+
+
 ## Introduction
 
 Target-Driven Distillation (TDD) features three key designs, that differ from previous consistency distillation methods.