Instructions to use Gerchegg/hyphoria_qwen_v1-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gerchegg/hyphoria_qwen_v1-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Gerchegg/hyphoria_qwen_v1-Diffusers", 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
| license: apache-2.0 | |
| base_model: | |
| - Qwen/Qwen-Image | |
| pipeline_tag: text-to-image | |
| library_name: diffusers | |
| tags: | |
| - qwen-image | |
| - diffusers | |
| - safetensors | |
| - hyphoria | |
| # Hyphoria Qwen v1 Diffusers | |
| Diffusers-packaged version of `chapel/hyphoria_qwen_v1.0` | |
| using `hyphoria_qwen_v1_bf16.safetensors` as the custom Qwen-Image transformer. | |
| Packaged analogously to `Gerchegg/Qwen_FloVector_Hex_CharactersScenes_V1-Diffusers`: | |
| - base components from `Qwen/Qwen-Image`: `scheduler/`, `text_encoder/`, `tokenizer/`, `vae/` | |
| - custom transformer weights at `transformer/diffusion_pytorch_model.safetensors` | |
| - Qwen-Image transformer config at `transformer/config.json` | |
| Source checkpoint: https://huggingface.co/chapel/hyphoria_qwen_v1.0 | |