Instructions to use Balta10/fire with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Balta10/fire with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Balta10/fire", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download model_index.json from Balta10/fire: direct link, hf CLI and curl.
- Browser
- Download file 573 Bytes
-
https://huggingface.co/Balta10/fire/resolve/main/model_index.json
- Command line
-
hf download hf://Balta10/fire/model_index.json
-
curl -L -o model_index.json https://huggingface.co/Balta10/fire/resolve/main/model_index.json
573 Bytes
| { | |
| "_class_name": "QwenImageEditPlusPipeline", | |
| "_diffusers_version": "0.37.0.dev0", | |
| "_name_or_path": "FireRedTeam/FireRed-Image-Edit-1.0", | |
| "processor": [ | |
| "transformers", | |
| "Qwen2VLProcessor" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "Qwen2_5_VLForConditionalGeneration" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "Qwen2Tokenizer" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "QwenImageTransformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKLQwenImage" | |
| ] | |
| } | |