Text-to-Image
Diffusers
Safetensors
English
Chinese
QwenImage21Pipeline
sdnq
int4
uint4
image-generation
image-editing
apple-silicon
8-bit precision
Instructions to use ixim/Image21-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ixim/Image21-INT4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ixim/Image21-INT4", 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
File size: 1,228 Bytes
9116984 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | """Editing inputs: independent noise and unchanged RGBA pixels."""
import math
from pathlib import Path
from PIL import Image, ImageOps
DEFAULT_SEEDS = (1000042, 1000123)
def validate_seeds(cases, seeds):
if (not seeds or len(set(seeds)) != len(seeds)
or any(type(s) is not int or not 0 <= s < 2**63 for s in seeds)):
raise ValueError('Use distinct integer seeds in [0, 2**63)')
source_seeds = {c.get('source_seed') for c in cases} - {None}
if source_seeds.intersection(seeds):
raise ValueError('Editing seeds must differ from every known source seed (noise replay)')
return list(seeds)
def load_input(path):
with Image.open(path) as image:
image = ImageOps.exif_transpose(image)
has_alpha = 'A' in image.getbands() or 'transparency' in image.info
return image.convert('RGBA' if has_alpha else 'RGB')
def edit_dimensions(size, resolution):
if resolution < 32 or resolution % 32 or min(size) <= 0:
raise ValueError('Positive image size and resolution divisible by 32 required')
ratio = size[0] / size[1]
width = math.sqrt(resolution**2 * ratio)
return max(32, round(width / 32) * 32), max(32, round(width / ratio / 32) * 32)
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