AryamaR/Sana_Dreambooth_LoRa_YarnArtFrog

Model Card for Model ID

import torch from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained( "Efficient-Large-Model/Sana_600M_512px_diffusers", variant="fp16", torch_dtype=torch.float16, )

Load LoRA weights

pipe.load_lora_weights(lora_weights_path)

Set scheduler

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

Move to GPU

pipe.to("cuda")

pipe.vae.to(torch.bfloat16)

pipe.text_encoder.to(torch.bfloat16)

Prompt

prompt = 'A cute frog eating flies, in yarn art style' image = pipe( prompt=prompt, height=512, width=512, guidance_scale=4.5, num_inference_steps=20, generator=torch.Generator(device="cuda").manual_seed(42), )[0]

image[0].show()

Model Details

Model Description

• Model type:
• **Finetuned from model : "Efficient-Large-Model/Sana_600M_512px_diffusers"

Reference: https://github.com/NVlabs/Sana/blob/main/asset/docs/sana_lora_dreambooth.md

prompt="A photo of sks frog in a pond, yarn art style"

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Citation

cff-version: 1.2.0 title: 'SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer' message: >- If you use this software or research, please cite it using the metadata from this file. type: misc authors:

• given-names: Enze family-names: Xie
• given-names: Junsong family-names: Chen
• given-names: Junyu family-names: Chen
• given-names: Han family-names: Cai
• given-names: Haotian family-names: Tang
• given-names: Yujun family-names: Lin
• given-names: Zhekai family-names: Zhang
• given-names: Muyang family-names: Li
• given-names: Ligeng family-names: Zhu
• given-names: Yao family-names: Lu
• given-names: Song family-names: Han repository-code: 'https://github.com/NVlabs/Sana' abstract: >- SANA proposes an efficient linear Diffusion Transformer (DiT) for high-resolution image synthesis, featuring a depth-growth paradigm, model pruning techniques, and inference-time scaling strategies to reduce training costs while maintaining generation quality. SANA-Sprint also achieves one-step generation of high-resolution images keywords:
• deep-learning
• diffusion-models
• transformer
• image-generation
• text-to-image
• efficient-training
• distillation license: Apache-2.0 version: 1.5.0 doi: 10.48550/arXiv.2410.10629 date-released: 2024-10-16

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