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--- |
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base_model: black-forest-labs/FLUX.1-dev |
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library_name: diffusers |
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tags: |
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- flux |
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- flux-diffusers |
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- text-to-image |
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- diffusers |
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- controlnet |
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- diffusers-training |
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- flux |
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- flux-diffusers |
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- text-to-image |
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- diffusers |
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- controlnet |
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- diffusers-training |
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inference: true |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# promeai/FLUX.1-controlnet-lineart-promeai |
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`promeai/FLUX.1-controlnet-lineart-promeai` holds controlnet weights trained on black-forest-labs/FLUX.1-dev with lineart condition. |
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Here are some example images. |
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prompt: cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere |
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| input-control | result image | |
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## Intended uses & limitations |
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## How to use |
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### with diffusers |
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```python |
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# TODO: add an example code snippet for running this diffusion pipeline |
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import torch |
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from diffusers.utils import load_image |
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from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline |
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from diffusers.models.controlnet_flux import FluxControlNetModel |
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base_model = 'black-forest-labs/FLUX.1-dev' |
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controlnet_model = 'promeai/FLUX.1-controlnet-lineart-promeai' |
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controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16) |
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pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16) |
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pipe.to("cuda") |
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control_image = load_image("./images/example-control.jpg") |
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prompt = "cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere" |
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image = pipe( |
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prompt, |
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control_image=control_image, |
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controlnet_conditioning_scale=0.6, |
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num_inference_steps=28, |
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guidance_scale=3.5, |
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).images[0] |
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image.save("./image.jpg") |
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``` |
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### with comfyui |
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An [example comfyui workflow](./example_workflow.json)is also provided. |
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## Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training details |
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This controlnet is trained on one A100-80G GPU, with carefully selected proprietary real-world images dataset, with imagesize 512 + batchsize 3 (earlier period), and imagesize 1024 + batchsize 1 (after 512 training). With above configs, the GPU memory was about 70G and takes around 3 days to get this 14000steps-checkpoint. |
