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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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--- |
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# dee-z-image |
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This repository hosts a text-to-image checkpoint in Diffusers format. It is compatible with `ZImagePipeline` and can be loaded directly from the Hugging Face Hub. |
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## Usage |
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### Install |
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Install the latest Diffusers (recommended) and the required runtime dependencies: |
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```bash |
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pip install -U torch transformers accelerate safetensors |
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pip install -U diffusers |
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``` |
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If your installed Diffusers version does not include `ZImagePipeline`, install Diffusers from source instead: |
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```bash |
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pip install -U git+https://github.com/huggingface/diffusers |
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``` |
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### Generate an image |
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```python |
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import torch |
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from diffusers import ZImagePipeline |
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model_id = "telcom/dee-z-image" |
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pipe = ZImagePipeline.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, # use torch.float16 if your GPU does not support bf16 |
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low_cpu_mem_usage=False, |
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) |
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pipe.to("cuda") |
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prompt = "A cinematic studio photo of a small robot sitting at a desk, warm lighting, shallow depth of field, high detail." |
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image = pipe( |
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prompt=prompt, |
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height=1024, |
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width=1024, |
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num_inference_steps=9, |
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guidance_scale=0.0, |
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generator=torch.Generator("cuda").manual_seed(42), |
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).images[0] |
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image.save("out.png") |
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``` |
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## Tips |
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- If you run out of VRAM, try `pipe.enable_model_cpu_offload()` (requires `accelerate`) or reduce the resolution. |
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- Start with `guidance_scale=0.0` and `num_inference_steps` around 8–12; adjust based on quality/speed needs. |
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- For reproducibility, set a `generator` seed as shown above. |
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## Repository contents |
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- `model_index.json` defines the Diffusers pipeline components used by `ZImagePipeline`. |
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- `text_encoder/`, `tokenizer/`, `transformer/`, `vae/`, `scheduler/` contain the model submodules. |
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- `assets/` contains example images and an optional gallery PDF. |
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## License |
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Apache-2.0 (see metadata at the top of this model card). |
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## Acknowledgements |
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This repo packages a checkpoint for the Z-Image family of models. For upstream project details, see: |
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- https://github.com/Tongyi-MAI/Z-Image |
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- https://arxiv.org/abs/2511.22699 |
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