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--- |
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license: creativeml-openrail-m |
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library_name: diffusers |
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pipeline_tag: text-to-image |
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--- |
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The model is created using the following steps: |
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- Find the desired model (checkpoint or lora) on Civitai |
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- You can see some conversion scripts in diffusesrs. This time, only the scripts for converting checkpoit and lora are used. |
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It depends on the model type of Civitai. If it is a lora model, you need to specify a basic model |
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- Using __load_lora function from https://towardsdatascience.com/improving-diffusers-package-for-high-quality-image-generation-a50fff04bdd4 |
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``` |
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from diffusers import DiffusionPipeline |
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import torch |
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pipeline = DiffusionPipeline.from_pretrained( |
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"Andyrasika/lora_diffusion" |
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,custom_pipeline = "lpw_stable_diffusion" |
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,torch_dtype=torch.float16 |
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) |
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lora = ("/content/lora_model.safetensors",0.8) |
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pipeline = __load_lora(pipeline=pipeline,lora_path=lora[0],lora_weight=lora[1]) |
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pipeline.to("cuda") |
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# pipeline.enable_xformers_memory_efficient_attention() |
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#https://huggingface.co/docs/diffusers/optimization/fp16 |
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pipeline.enable_vae_tiling() |
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prompt = """ |
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shukezouma,negative space,shuimobysim |
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a branch of flower, traditional chinese ink painting |
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""" |
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image = pipeline(prompt).images[0] |
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image |
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``` |
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<hr> |
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Since this is only the first official release, I believe there are still many, many imperfections. |
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Please provide feedback in time, and I will continuously make corrections, thank you! |
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