chore: clean debugging scripts

debug/flux.1-fill-dev16.py

-import torch
-from diffusers import FluxFillPipeline
-from diffusers.utils import load_image
-
-from nunchaku import NunchakuFluxTransformer2dModel
-from nunchaku.utils import get_precision
-
-image = load_image("./removal_image.png")
-mask = load_image("./removal_mask.png")
-
-precision = get_precision()  # auto-detect your precision is 'int4' or 'fp4' based on your GPU
-pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16)
-# import ipdb
-
-# ipdb.set_trace()
-pipe.load_lora_weights(
-    "./loras/removalV2.safetensors"
-)  # Path to your LoRA safetensors, can also be a remote HuggingFace path
-pipe.fuse_lora(lora_scale=1)
-pipe.enable_model_cpu_offload()
-
-image = pipe(
-    prompt="",
-    image=image,
-    mask_image=mask,
-    height=720,
-    width=1280,
-    guidance_scale=30,
-    num_inference_steps=20,
-    max_sequence_length=512,
-    generator=torch.Generator().manual_seed(42),
-).images[0]
-image.save(f"flux.1-fill-dev-bf16.png")

debug/flux.1-fill-dev4.py

-import torch
-from diffusers import FluxFillPipeline
-from diffusers.utils import load_image
-
-from nunchaku import NunchakuFluxTransformer2dModel
-from nunchaku.utils import get_precision
-
-image = load_image("./removal_image.png")
-mask = load_image("./removal_mask.png")
-
-precision = get_precision()  # auto-detect your precision is 'int4' or 'fp4' based on your GPU
-transformer = NunchakuFluxTransformer2dModel.from_pretrained(f"mit-han-lab/svdq-{precision}-flux.1-fill-dev")
-### LoRA Related Code ###
-transformer.update_lora_params(
-    "loras/removalV2.safetensors"
-)  # Path to your LoRA safetensors, can also be a remote HuggingFace path
-transformer.set_lora_strength(1)  # Your LoRA strength here
-### End of LoRA Related Code ###
-
-pipe = FluxFillPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-Fill-dev", transformer=transformer, torch_dtype=torch.bfloat16
-).to("cuda")
-image = pipe(
-    prompt="",
-    image=image,
-    mask_image=mask,
-    height=720,
-    width=1280,
-    guidance_scale=30,
-    num_inference_steps=20,
-    max_sequence_length=512,
-    generator=torch.Generator().manual_seed(42),
-).images[0]
-image.save(f"flux.1-fill-dev-{precision}.png")

debug/get_image_and_mask.py

-from PIL import Image, ImageChops
-
-# 打开 RGBA 图像
-img = Image.open("removal.png").convert("RGBA")
-
-# 拆分成 R, G, B, A 四个通道
-r, g, b, a = img.split()
-a_inverted = ImageChops.invert(a)
-
-# 合并 R, G, B 成 RGB 图像
-rgb_img = Image.merge("RGB", (r, g, b))
-
-# 保存 RGB 和 A 分开的图像
-rgb_img.save("removal_image.png")
-a_inverted.save("removal_mask.png")

debug/split_lora.py

-import torch
-from safetensors.torch import save_file
-
-from nunchaku.utils import load_state_dict_in_safetensors
-
-if __name__ == "__main__":
-    sd = load_state_dict_in_safetensors("loras/removalV2.safetensors")
-    new_sd = {}
-    for k, v in sd.items():
-        if ".single_transformer_blocks." in k:
-            new_sd[k] = v
-        else:
-            new_sd[k] = torch.zeros_like(v)
-    save_file(new_sd, "loras/removalV2-single.safetensors")