Instructions to use toxicdog/Ming-Image-0.1-Design-INT4-ConvRot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use toxicdog/Ming-Image-0.1-Design-INT4-ConvRot with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("toxicdog/Ming-Image-0.1-Design-INT4-ConvRot", 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
Add Ming-Image INT4 ConvRot model
Browse filesAdd a ComfyUI native ConvRot W4A4 conversion of the Ming-Image-0.1-Design diffusion transformer, with source details, usage notes, checksum, license, and Radiant Canvas testing link.
- LICENSE +21 -0
- README.md +58 -0
- ming_image_0.1_design_int4_convrot.safetensors +3 -0
LICENSE
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MIT License
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Copyright (c) 2026 inclusionAI
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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license: mit
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library_name: custom
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pipeline_tag: text-to-image
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base_model:
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- inclusionAI/Ming-Image-0.1-Design
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tags:
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- comfyui
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- image-generation
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- int4
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- convrot
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- quantized
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---
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# Ming-Image-0.1-Design INT4 ConvRot
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Repackaged model files for ComfyUI and others, with INT4 ConvRot converted for
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testing in Radiant Canvas for fast macOS inference:
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[Radiant Canvas on the App Store](https://apps.apple.com/us/app/radiant-canvas-ai-image-gen/id6802973075).
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The original model is
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[inclusionAI/Ming-Image-0.1-Design](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design).
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## File
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- `ming_image_0.1_design_int4_convrot.safetensors`: the diffusion transformer
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in ComfyUI's native ConvRot W4A4 format.
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For ComfyUI, place the file in:
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```text
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ComfyUI/models/diffusion_models/
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```
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Use a recent ComfyUI build with Ming-Image and native INT4 ConvRot support. The
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text model, connector, MLP, and VAE are not included in this repository. Use
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the original model files or another compatible ComfyUI package for those
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components.
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## Conversion details
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- Source revision: `208087ada1486931692c1896f38d4cd16ff3df82`
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- Source format: BF16, five transformer shards, 519 tensors
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- Quantized layers: 102 feed-forward weights
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- ConvRot group size: 256
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- Quantization group size: 64
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- Sensitive attention, input, output, modulation, and normalization layers are
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kept in BF16 for compatibility.
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- Output size: 6,296,983,672 bytes
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- SHA-256: `faaa099bfd8ecc704bbf2655a9bf78d80bf57f26df172bedd6f07d27cb114d36`
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This is a test conversion. Check output quality for your own prompts and
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workflows before relying on it.
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## License
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The original model is released under the MIT License. A copy is included in
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this repository.
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ming_image_0.1_design_int4_convrot.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:faaa099bfd8ecc704bbf2655a9bf78d80bf57f26df172bedd6f07d27cb114d36
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size 6296983672
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