Update README.md

README.md

@@ -13,82 +13,14 @@ tags:
 
 # Trail Delighter — FLUX.2 Klein 4B LoRA
 
-This is a source-conditioned LoRA for **FLUX.2 Klein 4B**. It was trained for
-one narrow task: reducing cast shadows, glare, and uneven illumination in
-trail-surface photographs while preserving the camera viewpoint and physical
-scene structure.
+This is a LoRA for **FLUX.2 Klein 4B** specifically for the task of giving natural light to mountain bike trail photographs.
 
-It is not a general-purpose shadow-removal model. The training set consists of
-real Chunk 0 trail photographs paired with Qwen/Fal lighting-restoration
-outputs. The model is intended for texture-capture imagery and may perform
-poorly on unrelated photographs.
-
-## Files
-
-- `training/trail_delight_klein4b_highres15.safetensors` — final LoRA
-- `training/checkpoints/` — 125, 250, and 375 step checkpoints
-- `examples/teacher_pairs/original/` — original trail photos
-- `examples/teacher_pairs/qwen_delighted/` — Qwen/Fal teacher outputs
-- `examples/teacher_pairs/klein_lora/` — corresponding Klein LoRA outputs
-
-The base FLUX.2 Klein weights are not included. Download them from the
-upstream model repository separately.
-
-## Inference with Diffusers
-
-```python
-import torch
-from diffusers import Flux2KleinPipeline
-from diffusers.utils import load_image
-
-base = "black-forest-labs/FLUX.2-klein-4B"
-lora = "DirtScan/trail-delighter"
-
-pipe = Flux2KleinPipeline.from_pretrained(base, torch_dtype=torch.float16)
-pipe.load_lora_weights(lora, subfolder="training", adapter_name="trail_delight")
-pipe.set_adapters(["trail_delight"], adapter_weights=[1.0])
-pipe.enable_sequential_cpu_offload(device="cuda")
-pipe.vae.enable_tiling()
-pipe.vae.enable_slicing()
-
-image = load_image("input-trail-photo.png").convert("RGB").resize((768, 448))
-result = pipe(
-    image=image,
-    prompt=(
-        "Edit this trail-surface photograph under neutral soft overcast illumination. "
-        "Remove cast shadows and glare while preserving the exact trail geometry, "
-        "rocks, leaves, markings, materials, and camera viewpoint."
-    ),
-    width=768,
-    height=448,
-    num_inference_steps=4,
-    guidance_scale=1.0,
-).images[0]
-result.save("trail-delighted.png")
-```
-
-For local inference, the final LoRA is small, but the unquantized base model
-is substantially larger than an 8 GB GPU. Sequential CPU offload or a
-compatible quantized runtime may be required.
-
-## Training summary
-
-- Base: FLUX.2 Klein 4B
-- Adapter: LoRA, rank 16
-- Teacher: Qwen Image Edit lighting-restoration via Fal
-- Teacher pairs: 15 real Chunk 0 trail-photo pairs
-- Resolution used for the student run: 768 × 448
-- Final checkpoint: 500 training steps
-
-## Limitations
-
-The adapter can still produce image-to-image hue variation, local texture
-changes, or residual seams when many independently processed views are merged
-into a texture atlas. It should therefore be followed by atlas-level colour
-harmonization and seam-aware blending for 3D texturing.
+The process was as follows:
+- Photos are taken of a mountain bike trail (often sunny, cast shadows, lens flare, glare)
+- A selection of these were de-lighted using https://fal.ai/models/fal-ai/qwen-image-edit-2509-lora-gallery/lighting-restoration - This creates a "teacher set"
+- This teacher set was then used to train a new LoRA, but with a base of **FLUX.2 Klein 4B** (instead of Qwen) - overall making the model smaller and easier to run.
 
 ## License
 
 The adapter is provided under Apache-2.0, subject to the terms of the base
-FLUX.2 Klein model and the rights to the training photographs and teacher
-outputs.
+FLUX.2 Klein model.