ABDALLALSWAITI
/

Maxwell

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-library_name: diffusers
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+## license: creativeml-openrail-m
+This model may be used by individuals for personal and commercial purposes, including generating and selling images.
+Commercial use by companies or organizations is strictly prohibited.
+# Maxwell Model
+## Acknowledgements
+Firstly, a big thanks to [@sayakpaul](https://huggingface.co/sayakpaul) who fixed most issues we were facing with Diffusers.
+## Installation
+1. Download the repository at the same level as the generative code.
+2. Install the required packages:
+```bash
+pip install torch accelerate safetensors diffusers huggingface_hub huggingface_hub bitsandbytes transformers
+```
+install [convert_nf4_flux.py](https://huggingface.co/ABDALLALSWAITI/Maxwell/resolve/main/convert_nf4_flux.py?download=true)
+@same level of Generative Code
+## Usage
+Run the following Python code:
+```python
+# Generative Code
+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
+# Set dtype and check for float8 support
+dtype = torch.bfloat16
+is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
+# Download the model checkpoint
+ckpt_path = hf_hub_download("ABDALLALSWAITI/Maxwell", filename="diffusion_pytorch_model.safetensors")
+original_state_dict = safetensors.torch.load_file(ckpt_path)
+# Initialize the model with empty weights
+with init_empty_weights():
+    config = FluxTransformer2DModel.load_config("ABDALLALSWAITI/Maxwell")
+    model = FluxTransformer2DModel.from_config(config).to(dtype)
+    expected_state_dict_keys = list(model.state_dict().keys())
+# Replace layers with NF4 quantized versions
+replace_with_bnb_linear(model, "nf4")
+# Load the state dict into the quantized model
+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
+        )
+# Clean up
+del original_state_dict
+gc.collect()
+# Print model size
+print(compute_module_sizes(model)[""] / 1024 / 1204)
+# Initialize the pipeline
+pipe = FluxPipeline.from_pretrained("black-forest-labs/flux.1-dev", transformer=model, torch_dtype=dtype)
+pipe.enable_model_cpu_offload()
+# Generate an image from a prompt
+prompt = "A mystic Tiger in  with sign that says hello world!"
+image = pipe(prompt, guidance_scale=0.0, num_inference_steps=4, generator=torch.manual_seed(0)).images[0]
+image.save("simple.png")
+```
+This code will download the Maxwell model, initialize it with NF4 quantization, and generate an image based on the given prompt.