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crash-report-framed2

Text-to-Image
Diffusers
stable-diffusion-xl
stable-diffusion-xl-diffusers
lora
template:sd-lora
Model card Files Community
crash-report-framed2
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---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:

        - text: 'a close up of a colorful circular object with a city expanding from it in the style of <s0><s1>, explosion of data fragments, isolated on white background, dendrites, 3d cell shaded, london, view from slightly above, atsmospheric, fully symmetrical, giant explosion, datamoshed, white background canvas, computer graphic with white background'
          output:
            url:
                "image_0.png"
        
        - text: 'a close up of a colorful circular object with a city expanding from it in the style of <s0><s1>, explosion of data fragments, isolated on white background, dendrites, 3d cell shaded, london, view from slightly above, atsmospheric, fully symmetrical, giant explosion, datamoshed, white background canvas, computer graphic with white background'
          output:
            url:
                "image_1.png"
        
        - text: 'a close up of a colorful circular object with a city expanding from it in the style of <s0><s1>, explosion of data fragments, isolated on white background, dendrites, 3d cell shaded, london, view from slightly above, atsmospheric, fully symmetrical, giant explosion, datamoshed, white background canvas, computer graphic with white background'
          output:
            url:
                "image_2.png"
        
        - text: 'a close up of a colorful circular object with a city expanding from it in the style of <s0><s1>, explosion of data fragments, isolated on white background, dendrites, 3d cell shaded, london, view from slightly above, atsmospheric, fully symmetrical, giant explosion, datamoshed, white background canvas, computer graphic with white background'
          output:
            url:
                "image_3.png"
        
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: 3d icon in the style of <s0><s1>
license: openrail++
---

# SDXL LoRA DreamBooth - backnotprop/crash-report-framed2

<Gallery />

## Model description

### These are backnotprop/crash-report-framed2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

## Download model

### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

- **LoRA**: download **[`crash-report-framed2.safetensors` here 💾](/backnotprop/crash-report-framed2/blob/main/crash-report-framed2.safetensors)**.
    - Place it on your `models/Lora` folder.
    - On AUTOMATIC1111, load the LoRA by adding `<lora:crash-report-framed2:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`crash-report-framed2_emb.safetensors` here 💾](/backnotprop/crash-report-framed2/blob/main/crash-report-framed2_emb.safetensors)**.
    - Place it on it on your `embeddings` folder
    - Use it by adding `crash-report-framed2_emb` to your prompt. For example, `3d icon in the style of crash-report-framed2_emb`
    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
    

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('backnotprop/crash-report-framed2', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='backnotprop/crash-report-framed2', filename='crash-report-framed2_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('a close up of a colorful circular object with a city expanding from it in the style of <s0><s1>, explosion of data fragments, isolated on white background, dendrites, 3d cell shaded, london, view from slightly above, atsmospheric, fully symmetrical, giant explosion, datamoshed, white background canvas, computer graphic with white background').images[0]
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

## Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept `TOK` → use `<s0><s1>` in your prompt 



## Details
All [Files & versions](/backnotprop/crash-report-framed2/tree/main).

The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.