Instructions to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4", dtype=torch.bfloat16, device_map="cuda") prompt = "a puppy in a pond, yarn art style" image = pipe(prompt).images[0] - Sana
How to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
End of training
Browse files- .gitattributes +4 -0
- README.md +67 -0
- falqon-checkpoint-700/falqon_weights.pt +3 -0
- falqon-checkpoint-700/training_args.pt +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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image_2.png filter=lfs diff=lfs merge=lfs -text
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image_3.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: Efficient-Large-Model/Sana_1600M_1024px_diffusers
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library_name: diffusers
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license: other
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instance_prompt: a puppy, yarn art style
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widget:
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- text: a puppy in a pond, yarn art style
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output:
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url: image_0.png
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- text: a puppy in a pond, yarn art style
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output:
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url: image_1.png
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- text: a puppy in a pond, yarn art style
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output:
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url: image_2.png
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- text: a puppy in a pond, yarn art style
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output:
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url: image_3.png
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tags:
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- text-to-image
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- diffusers-training
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- diffusers
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- falqon
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- sana
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- sana-diffusers
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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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# Sana DreamBooth FALQON - wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4
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<Gallery />
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## Model description
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These are wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 DreamBooth FALQON weights for Efficient-Large-Model/Sana_1600M_1024px_diffusers.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with FALQON
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(FP8-Accelerated LoRA with Quantization) for efficient fine-tuning.
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## Trigger words
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You should use `a puppy, yarn art style` to trigger the image generation.
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## License
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Please check the base model license.
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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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]
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falqon-checkpoint-700/falqon_weights.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:b57fb10e2467b0a2995dd365b4599512f9d76256e4ca85ea7b79deb4ba6c012a
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size 699636658
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falqon-checkpoint-700/training_args.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:61d6aa1a77fdeaead8fcc7baad12ea632fc9725eddc8c3c8535f3cc56fb61703
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size 3128
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image_0.png
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Git LFS Details
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image_1.png
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image_2.png
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Git LFS Details
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image_3.png
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Git LFS Details
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