init commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+T2I.png filter=lfs diff=lfs merge=lfs -text
+inpaint.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,79 @@
+---
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
+language:
+- en
+base_model: black-forest-labs/FLUX.1-dev
+library_name: diffusers
+tags:
+- Text-to-Image
+- FLUX
+- Stable Diffusion
+---
+
+<div style="display: flex; justify-content: center; align-items: center;">
+  <img src="./images_alibaba.png" alt="alibaba" style="width: 20%; height: auto; margin-right: 5%;">
+  <img src="./images_alimama.png" alt="alimama" style="width: 20%; height: auto;">
+</div>
+
+This repository provides a 8-step distilled lora for [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model released by AlimamaCreative Team.
+
+# Description
+This checkpoint is a 8-step distilled Lora, trained based on FLUX.1-dev model. We use a multi-head discriminator to improve the distill quality. Our model can be used for T2I, inpainting controlnet and other FLUX related models. The recommended guidance_scale=3.5 and lora_scale=1. Our Lower steps version will release later.
+
+- Text-to-Image.
+
+![](./T2I.png)
+
+- With [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://huggingface.co/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta). Our distilled lora can be well adapted to the Inpainting controlnet, and the accelerated generated effect can follow the original output well.
+
+![](./inpaint.png)
+
+# How to use
+## diffusers
+This model can be used ditrectly with diffusers
+
+```json
+import torch
+from diffusers.pipelines import FluxPipeline
+
+model_id = "black-forest-labs/FLUX.1-dev"
+adapter_id = "alimama-creative/FLUX.1-Turbo-Alpha"
+
+pipe = FluxPipeline.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+prompt = "A DSLR photo of a shiny VW van that has a cityscape painted on it. A smiling sloth stands on grass in front of the van and is wearing a leather jacket, a cowboy hat, a kilt and a bowtie. The sloth is holding a quarterstaff and a big book."
+image = pipe(
+            prompt=prompt,
+            guidance_scale=3.5,
+            height=1024,
+            width=1024,
+            num_inference_steps=8,
+            max_sequence_length=512).images[0]
+```
+
+## comfyui
+
+- T2I turbo workflow: [click here](./t2I_flux_turbo.json)
+- Inpainting controlnet turbo workflow: [click here](./alimama_flux_inpainting_turbo_8step.json)
+
+
+# Training Details
+
+The model is trained on 1M open source and internal sources images, with the aesthetic 6.3+ and resolution greater than 800. We use adversarial training to improve the quality. Our method fix the original FLUX.1-dev transformer as the discriminator backbone, and add multi heads to every transformer layer. We fix the guideline scale as 3.5 during training, and use the time shift as 3. 
+
+Mixed precision: bf16
+
+Learning rate: 2e-5
+
+Batch size: 64
+
+Image size: 1024x1024

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