Try it: huggingface.co/ISLAM-PO/Glitch-Img-1.0 · image-gen demo Space
Image generation has moved past "pretty pictures" toward controllable, editable, transparent assets. Glitch-Img-1.0 is my re-upload of Qwen/Qwen-Image-2.1 under the name ISLAM-PO/Glitch-Img-1.0, aimed at making a strong unified text-to-image + editing model easy to discover and use from a single, well-documented repository. All weights and configs are unchanged from the original; only the README and repo IDs were updated, and the model is Built with Qwen under the Qwen Research License (non-commercial).
This report covers what the model does, its key capabilities, a quick-start, memory tips, and licensing notes.
Qwen/Qwen-Image-2.1
text-to-image (diffusers: QwenImage21Pipeline)
Four headline improvements define the Qwen-Image-2.1 family this build inherits:
| Capability | Details |
|---|---|
| Text-to-image | Prompts up to 2048×2048 and beyond; strong text rendering |
| Image editing | Inpainting-style edits, background swaps, style transfer |
| RGBA transparency | Native alpha channel generation with a documented prompt format |
| Multi-reference | Up to 10 reference images for identity/subject consistency |
| Local edits | Circles, painted annotations, separate masks |
| Aspect ratios | 1:1, 4:3, 3:4, 3:2, 2:3, 16:9, 9:16 presets |
Install:
pip install torch>=2.4.0
pip install transformers>=5.17
pip install git+https://github.com/huggingface/diffusers
pip install accelerate pillow
Text-to-image:
import torch
from diffusers import QwenImage21Pipeline
pipe = QwenImage21Pipeline.from_pretrained(
"ISLAM-PO/Glitch-Img-1.0", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
prompt='A neon shop sign that reads "GLITCH IMG 1.0", rainy night, reflections on wet pavement',
width=2048, height=2048,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("t2i_example.png")
Image editing:
import torch
from PIL import Image
from diffusers import QwenImage21Pipeline
pipe = QwenImage21Pipeline.from_pretrained(
"ISLAM-PO/Glitch-Img-1.0", torch_dtype=torch.bfloat16
).to("cuda")
input_image = Image.open("input.png")
image = pipe(
prompt="Change the background to a sunset beach",
image=input_image,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("edit_example.png")
Transparent (RGBA) generation — use the recommended prompt format:
image = pipe(
prompt="This is an RGBA image with transparency. A cute cartoon dragon sticker. "
"The image has alpha channel and the background is transparent.",
width=2048, height=2048,
num_inference_steps=40,
generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("transparent_example.png")
Aspect ratios:
aspect_ratios = {
"1:1": (2048, 2048), "4:3": (2400, 1792), "3:4": (1792, 2400),
"3:2": (2528, 1696), "2:3": (1696, 2528), "16:9": (2752, 1536),
"9:16": (1536, 2752),
}
pipe = QwenImage21Pipeline.from_pretrained(
"ISLAM-PO/Glitch-Img-1.0", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
This offloads model parts to CPU between steps, letting you run on smaller GPUs at the cost of speed.
I use Glitch-Img as the visual layer behind my portfolio and product work:
It complements MasryGPT (text) as the two halves of an Arabic-first AI product stack: one generates the words, the other the visuals.
model-business@notice.qwencloud.com. NOTICE and LICENSE in the repo for required attribution.
Author: Islam Al-Nashar — Full-Stack Developer & AI builder. GitHub · Hugging Face · Portfolio