import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Yntec/ChunkingChipShots", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Chunking Chip Shots
Warning: This model doesn't have a Diffusers version available, if you need to use it on Google Colab, request it on the Community tab.
This model improves over Chip & DallE's compositions and over ChunkyCat's backgrounds. Work in progress.
Original pages:
https://huggingface.co/Yntec/Chip_n_DallE
https://huggingface.co/Yntec/ChunkyCat
https://civitai.com/models/141004?modelVersionId=156294 (cat mochi property - NyankoMotsiX)
https://civitai.com/models/171113/chunkyvolume?modelVersionId=192248
https://civitai.com/models/156546/dalle-anime-model
https://huggingface.co/Yntec/aBagOfChips
https://huggingface.co/Yntec/GoodLife
https://tensor.art/models/628276277415133426 (DucHaiten-GoldenLife)
https://civitai.com/models/60724?modelVersionId=67980 (KIDS ILLUSTRATIONS V2)
Recipe:
- SuperMerger Weight Sum Use MBW 0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1
Model A:
ChunkyCat
Model B:
Chip_n_DallE
Output:
ChunkingChipShots
Merge Block Weight Analysis
If each corresponding block was labeled as this:
G = GoodLife
D = DallEAnimeModel
V = ChunkyVolume
N = NyankoMotsiX
Then the weights of the model would read like this:
N,V,V,V,V,V,V,V,V,V,V,D,D,G,D,D,D,D,D,D,G,G,V,D,D,D
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