Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
anime
cartoon
art
Colorful
Vivid
Paintings
advokat
andite
stable-diffusion
stable-diffusion-1.5
stable-diffusion-diffusers
Instructions to use Yntec/Pastelcake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/Pastelcake with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/Pastelcake", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Create README.md
Browse files
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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pipeline_tag: text-to-image
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tags:
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- anime
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- cartoon
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- art
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- Colorful
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- Vivid
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- Paintings
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- advokat
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- andite
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- stable-diffusion
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- stable-diffusion-1.5
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- stable-diffusion-diffusers
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- diffusers
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- text-to-image
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base_model:
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- Yntec/PastelMix
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- Yntec/Cheesecake
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base_model_relation: merge
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---
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The joke is that Pastel translates as Cake in spanish, so yo dawg, I herd you like cake, so I put a pastel in your cake so you can cake while you pastel! Work in progress.
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Original pages:
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https://civitai.com/models/6550?modelVersionId=7684 (MapleSyrup)
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https://huggingface.co/Yntec/Tantrum
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https://civitai.com/models/5414/pastel-mix-stylized-anime-model
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# 8008a and PastelcakeAlpha
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These are softer, easier to prompt versions of Pastelcake. The former is preserved because its name matches the first five characters of its hash, the second is here in case you don't want a style so artistic and would rather have better eyes and an easier way of prompting the model. I prefer the finest details of Pastelcake and that's why I made it the default.
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# Recipes:
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- SuperMerger Weight sum Train Difference Use MBW 1,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,0,0,0,1,1,1,0,0,0
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Model A:
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Cheesecake
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Model B:
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PastelMix
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Output Model:
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8008a
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- SuperMerger Weight sum Train Difference Use MBW 1,0,1,0,0,1,1,1,1,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0
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Model A:
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Cheesecake
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Model B:
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PastelMix
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Output Model:
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PastelcakeAlpha
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- SuperMerger Weight sum Train Difference Use MBW 1,0,0,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0,0,0,0,0,0,0,0,0
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Model A:
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Cheesecake
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Model B:
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PastelMix
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Output Model:
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Pastelcake
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# Merge Block Weight Analysis
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If each corresponding block was labeled as this:
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T = Tantrum
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P = PastelMix
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M = Maple Syrup
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Then the weights of the model would read like this:
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P,M,M,T,P,P,P,P,P,P,P,P,P,T,P,P,P,M,M,M,M,M,M,M,M,M
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