Instructions to use e-n-v-y/envy-ink-swirl-xl-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use e-n-v-y/envy-ink-swirl-xl-01 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("e-n-v-y/envy-ink-swirl-xl-01") prompt = "photograph, cinematic color grading, dragon made of shiny crimson swirling smoke, underwater" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
c1ad0f0
1
Parent(s): 56f9542
Upload folder using huggingface_hub
Browse files- 4676475.jpeg +0 -0
- 4676480.jpeg +0 -0
- 4676481.jpeg +0 -0
- 4676497.jpeg +0 -0
- 4676501.jpeg +0 -0
- 4676502.jpeg +0 -0
- 4676539.jpeg +0 -0
- 4676566.jpeg +0 -0
- 4676570.jpeg +0 -0
- 4676624.jpeg +0 -0
- EnvyInkSwirlXL01.safetensors +3 -0
- README.md +97 -0
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EnvyInkSwirlXL01.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8ece98cf1b06ff85e5d3732a24c6ae9833082a6816368d0439dc261509bb2a1
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size 46702152
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README.md
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---
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license: other
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license_name: bespoke-lora-trained-license
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license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Sell&allowDerivatives=True&allowDifferentLicense=True
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tags:
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- text-to-image
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- stable-diffusion
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- lora
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- diffusers
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- template:sd-lora
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- ink
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- hyperrealism
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- style
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- liquid
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- swirl
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: swirling ink
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widget:
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- text: 'photograph, cinematic color grading, dragon made of shiny crimson swirling smoke, underwater'
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output:
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url: >-
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4676624.jpeg
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- text: 'photograph, cinematic color grading, death knight made of swirling ink'
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output:
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url: >-
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4676481.jpeg
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- text: 'photograph, cinematic color grading, prowler made of shiny orange swirling ink, underwater'
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output:
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url: >-
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4676539.jpeg
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- text: 'photograph, cinematic color grading, miner made of swirling ink'
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output:
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url: >-
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4676475.jpeg
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- text: 'photograph, cinematic color grading, shadowdancer made of subsurface scattering red swirling ink, underwater'
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output:
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url: >-
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4676502.jpeg
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- text: 'photograph, cinematic color grading, dragon made of glimmering dark goldenrod swirling ink, underwater'
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output:
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url: >-
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4676566.jpeg
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- text: 'photograph, cinematic color grading, prisoner made of ultra dark orange swirling ink, underwater'
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output:
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url: >-
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4676501.jpeg
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- text: 'photograph, cinematic color grading, ferret made of swirling ink'
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output:
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url: >-
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4676480.jpeg
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- text: 'photograph, cinematic color grading, singer made of sparkling multicolor swirling ink'
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output:
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url: >-
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4676497.jpeg
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- text: 'photograph, cinematic color grading, dragon made of ultra light blue swirling ink, in the clouds'
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output:
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url: >-
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4676570.jpeg
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---
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# Envy Ink Swirl XL 01
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<Gallery />
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## Model description
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<p>Makes everything out of swirling ink. Keep your prompts relatively short for best results.</p>
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## Trigger words
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You should use `swirling ink` to trigger the image generation.
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](/e-n-v-y/envy-ink-swirl-xl-01/tree/main) them in the Files & versions tab.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
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pipeline.load_lora_weights('e-n-v-y/envy-ink-swirl-xl-01', weight_name='EnvyInkSwirlXL01.safetensors')
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image = pipeline('photograph, cinematic color grading, dragon made of ultra light blue swirling ink, in the clouds').images[0]
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```
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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)
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