Update README.md

README.md

@@ -1,4 +1,73 @@
----
-license: mit
-library_name: diffusers
----
+---
+license: mit
+library_name: diffusers
+---
+# flux-uncensored-nf4
+
+## Summary
+Flux base model merged with uncensored LoRA, quantized to NF4. This model is not for those looking for "safe" or watered-down outputs. It’s optimized for real-world use with fewer constraints and lower VRAM requirements, thanks to NF4 quantization.
+
+## Specs
+* Model: Flux base
+* LoRA: Uncensored version, merged directly
+* Quantization: NF4 format for speed and VRAM efficiency
+
+## Usage
+Not so much for plug-and-play model, but pretty straight forward (script from sayak [https://github.com/huggingface/diffusers/issues/9165#issue-2462431761])
+
+Please install pip install -U bitsandbytes to proceed.
+```python
+"""
+Some bits are from https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py
+"""
+
+from huggingface_hub import hf_hub_download
+from accelerate.utils import set_module_tensor_to_device, compute_module_sizes
+from accelerate import init_empty_weights
+from convert_nf4_flux import _replace_with_bnb_linear, create_quantized_param, check_quantized_param
+from diffusers import FluxTransformer2DModel, FluxPipeline
+import safetensors.torch
+import gc
+import torch
+
+dtype = torch.bfloat16
+is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
+ckpt_path = hf_hub_download("shauray/flux.1-dev-uncensored-nf4", filename="diffusion_pytorch_model.safetensors")
+original_state_dict = safetensors.torch.load_file(ckpt_path)
+
+with init_empty_weights():
+    config = FluxTransformer2DModel.load_config("shauray/flux.1-dev-uncensored-nf4")
+    model = FluxTransformer2DModel.from_config(config).to(dtype)
+    expected_state_dict_keys = list(model.state_dict().keys())
+
+_replace_with_bnb_linear(model, "nf4")
+
+for param_name, param in original_state_dict.items():
+    if param_name not in expected_state_dict_keys:
+        continue
+    
+    is_param_float8_e4m3fn = is_torch_e4m3fn_available and param.dtype == torch.float8_e4m3fn
+    if torch.is_floating_point(param) and not is_param_float8_e4m3fn:
+        param = param.to(dtype)
+    
+    if not check_quantized_param(model, param_name):
+        set_module_tensor_to_device(model, param_name, device=0, value=param)
+    else:
+        create_quantized_param(
+            model, param, param_name, target_device=0, state_dict=original_state_dict, pre_quantized=True
+        )
+
+del original_state_dict
+gc.collect()
+
+print(compute_module_sizes(model)[""] / 1024 / 1204)
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/flux.1-dev", transformer=model, torch_dtype=dtype)
+pipe.enable_model_cpu_offload()
+
+prompt = "A mystic cat with a sign that says hello world!"
+image = pipe(prompt, guidance_scale=3.5, num_inference_steps=50, generator=torch.manual_seed(0)).images[0]
+image.save("flux-nf4-dev-loaded.png")
+```
+
+this README has what you'd need, it's a merge from [Uncensored LoRA on CivitAI]([https://civitai.com/models/875879/flux-lustlyai-uncensored-v1-nsfw-lora-with-male-and-female-nudity)