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--- |
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base_model: runwayml/stable-diffusion-v1-5 |
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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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- 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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# controlnet-Jieya/model_out_canny |
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These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. |
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You can find some example images below. |
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prompt: gothic fractals |
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![images_0)](./images_0.png) |
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prompt: gothic fractals |
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![images_1)](./images_1.png) |
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prompt: gothic fractals |
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![images_2)](./images_2.png) |
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## Intended uses & limitations |
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#### How to use |
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```python |
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from datasets import Dataset |
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from pathlib import Path |
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from PIL import Image |
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face_image = Path("images/original") |
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canny_image = Path("images/canny_images") |
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def entry_for_id(entry_id): |
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if type(entry_id) == int: |
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entry_id = f"{entry_id:05}" |
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image = Image.open(face_image/f"fractal_{entry_id}.jpg") |
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image_cond = Image.open(canny_image/f"canny_fractal_{entry_id}.jpg") |
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caption = "gothic fractals" |
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return { |
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"image": image, |
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"canny_images": image_cond, |
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"caption": caption, |
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} |
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def generate_entries(): |
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for x in range(1, 6463): # Start from 1 and go up to 6462 |
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yield entry_for_id(x) |
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ds = Dataset.from_generator(generate_entries) |
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ds.push_to_hub('Jieya/fractal_image_6462') |
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``` |
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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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[TODO: describe the data used to train the model] |