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  - art
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  - tensorRT
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  ---
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- # Model Card for fast_sd_1.4
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- fast_sd_1.4 is a tensorRT implementation of Stable diffusion model, with stable 1.4 weights. Main features
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  - weights: original stable diffusion 1.4 weights
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- - input image size range: 100x100 ~ 200x200
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- - text encoder: clipxxx
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- - super-resultion: 4x by default.
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- - controlnet
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- - canny: yes
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- - depth(midas): yes
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- - xxx: yes
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-
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  ### Model Sources [optional]
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  ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Recommendations
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Glossary [optional]
 
 
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
 
 
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
 
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  - art
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  - tensorRT
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  ---
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+ # Model Card for fast_sd_1.5
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+ fast_sd_1.5 is a tensorRT implementation of Stable diffusion model, with stable 1.5 weights. Main features
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  - weights: original stable diffusion 1.4 weights
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+ - input image size range: 256~640 (min 256x256, max 640x640)
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+ - device requirements: Nvidia Ampere architecture or newer (A100)
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+ - super-resultion: 4x by default, optional.
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+ fast_sd_1.5 is a part of TME lyralab Fast Model Plan.
 
 
 
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  ### Model Sources [optional]
 
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  ## Uses
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+ ```python
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+ from fast_sd_1.5 import FSD
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+ from PIL import Image
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ inference_model = FSD(plan="path/to/plan/dir", superres="4x")
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+ # text input
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+ text = "A red ballon flying in the sky"
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+ output_img = inference_model.infer(text=text)
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+ # img input
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+ img = Image.open("test.jpg")
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+ output_img = inference_model.infer(img=img)
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+ ```
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