Text-to-Image
Diffusers
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Update README.md (#2)

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- Update README.md (6cf8a47a01b12f6f99c8babae8bfcb1501c861b1)


Co-authored-by: kuo <xiaopolu@users.noreply.huggingface.co>

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  1. README.md +32 -10
README.md CHANGED
@@ -23,17 +23,28 @@ Target-Driven Distillation: Consistency Distillation with Target Timestep Select
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  Samples generated by TDD-distilled SDXL, with only 4--8 steps.
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  </div>
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- ## Update
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- [2024.09.20]:Upload the TDD LoRA weights of FLUX-TDD-BETA(4-8-steps)
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- [2024.08.25]:Upload the TDD LoRA weights of SVD
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- [2024.08.22]:Upload the TDD LoRA weights of Stable Diffusion XL, YamerMIX and RealVisXL-V4.0, fast text-to-image generation.
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- - sdxl_tdd_lora_weights.safetensors
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- - yamermix_tdd_lora_weights.safetensors
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- - realvis_tdd_sdxl_lora_weights.safetensors
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-
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- 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.
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- ## Usage
 
 
 
 
 
 
 
 
 
 
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  You can directly download the model in this repository.
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  You also can download the model in python script:
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@@ -71,6 +82,17 @@ image = pipe(
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  image.save("tdd.png")
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  ```
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  ## Introduction
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  Target-Driven Distillation (TDD) features three key designs, that differ from previous consistency distillation methods.
 
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  Samples generated by TDD-distilled SDXL, with only 4--8 steps.
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  </div>
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+ ## Usage FLUX
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+ ```python
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+ from huggingface_hub import hf_hub_download
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+ from diffusers import FluxPipeline
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+
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+ pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
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+ pipe.load_lora_weights(hf_hub_download("RED-AIGC/TDD", "TDD-FLUX.1-dev-lora-beta.safetensors"))
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+ pipe.fuse_lora(lora_scale=0.125)
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+ pipe.to("cuda")
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+
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+ image_flux = pipe(
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+ prompt=[prompt],
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+ generator=torch.Generator().manual_seed(int(3413)),
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+ num_inference_steps=8,
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+ guidance_scale=2.0,
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+ height=1024,
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+ width=1024,
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+ max_sequence_length=256
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+ ).images[0]
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+ ```
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+ ## Usage SDXL
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  You can directly download the model in this repository.
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  You also can download the model in python script:
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  image.save("tdd.png")
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  ```
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+ ## Update
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+ [2024.09.20]:Upload the TDD LoRA weights of FLUX-TDD-BETA(4-8-steps)
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+ [2024.08.25]:Upload the TDD LoRA weights of SVD
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+ [2024.08.22]:Upload the TDD LoRA weights of Stable Diffusion XL, YamerMIX and RealVisXL-V4.0, fast text-to-image generation.
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+ - sdxl_tdd_lora_weights.safetensors
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+ - yamermix_tdd_lora_weights.safetensors
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+ - realvis_tdd_sdxl_lora_weights.safetensors
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+
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+ 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.
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+
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+
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  ## Introduction
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  Target-Driven Distillation (TDD) features three key designs, that differ from previous consistency distillation methods.