import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("shiyi123/firered")
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]FireRed flowchart best_v7 optimized assets
This repository stores the large runtime assets for deploying the flowchart erasing model. The code, service entrypoints, and experiment scripts are maintained in the Git repository.
Contents
- models/flowchart_v7_lora_best/: best_v7 LoRA adapter trained from the Lightning-initialized run.
- runtime/optimized/: recommended optimized inference dependencies and local wheels.
- service/default_config.json: default inference/service parameters.
- metadata/: validation and optimized benchmark metadata.
- eval/full_best_new_benchmark_optimized/: optimized benchmark run metadata.
Expected Base Model
The remote server is expected to already have the base model at pretrained_models/FireRed-Image-Edit-1.1.
If your server stores the base model elsewhere, set MODEL_PATH or edit service/default_config.json.
Default Inference Parameters
- Prompt: Remove all the bright green overlays
- Seed: 43
- Steps: 8
- True CFG scale: 1.0
- Guidance scale: 1.0
- Pipeline mode: optimized
- Optimized max side: 1664
- Max sequence length: 512
Notes
The full Python virtual environment is intentionally not uploaded because it is not portable across servers. Use runtime/optimized/requirements_optimized.txt and the wheels under runtime/optimized/wheels/ to rebuild the environment.
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