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---
library_name: diffusers
license: other
datasets:
- BleachNick/UltraEdit
pipeline_tag: text-to-image
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
StableDiffusion3 model trained with the UltraEdit data to perform the mask-based and free-form image editing.
You can get the modified verson of diffusers from the github [PAGE](https://github.com/HaozheZhao/UltraEdit):
`cd diffusers && pip install -e .`
And then you can run:
```python
# For Editing with SD3
import torch
from diffusers import StableDiffusion3InstructPix2PixPipeline
from diffusers.utils import load_image
import requests
import PIL.Image
pipe = StableDiffusion3InstructPix2PixPipeline.from_pretrained("BleachNick/SD3_UltraEdit_w_mask", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt="What if the horse wears a hat?"
img = load_image("input.png").resize((512, 512))
mask_img = load_image("mask_img.png").resize(img.size)
# For free form Editing, seed a blank mask
# mask_img = PIL.Image.new("RGB", img.size, (255, 255, 255))
image = pipe(
prompt,
image=img,
mask_img=mask_img,
negative_prompt="",
num_inference_steps=50,
image_guidance_scale=1.5,
guidance_scale=7.5,
).images[0]
image.save("edited_image.png")
# display image
```
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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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