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--- |
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base_model: CompVis/stable-diffusion-v1-4 |
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library_name: diffusers |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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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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# Text-to-image finetuning - jangmin/foodai-pipeline-ko |
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This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** with replacement of text encoder **Bingsu/my-korean-stable-diffusion-v1-5** on the **AI-HUB: 건강관리를 위한 음식 이미지** dataset. |
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## Pipeline usage |
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You can use the pipeline like so: |
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```python |
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from diffusers import StableDiffusionPipeline |
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import torch |
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# Set device |
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device = ( |
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"mps" |
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if torch.backends.mps.is_available() |
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else "cuda" |
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if torch.cuda.is_available() |
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else "cpu" |
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) |
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torch_dtype = torch.float16 if device == "cuda" else torch.float32 |
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pipeline = StableDiffusionPipeline.from_pretrained("jangmin/foodai-pipeline-ko", torch_dtype=torch_dtype) |
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pipeline.to(device) |
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prompt = "짜장면, 정면에서 본 사진, 그릇에 담긴" |
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image = pipeline(prompt, guidance_scale=8, num_inference_steps=35).images[0] |
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image |
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``` |
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## Training info |
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These are the key hyperparameters used during training: |
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* Epochs: 1 |
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* Learning rate: 1e-05 |
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* Batch size: 8 |
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* Gradient accumulation steps: 4 |
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* Image resolution:512 |
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* Mixed-precision: bf16 |
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