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--- |
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language: |
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- en |
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library_name: diffusers |
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license: other |
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license_name: flux-1-dev-non-commercial-license |
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license_link: LICENSE.md |
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--- |
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# FLUX.1-merged |
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This repository provides the merged params for [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev) |
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and [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell). Please be aware of the licenses of both |
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the models before using the params commercially. |
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<table> |
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<thead> |
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<tr> |
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<th>Dev (50 steps)</th> |
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<th>Dev (4 steps)</th> |
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<th>Dev + Schnell (4 steps)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td> |
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<img src="./assets/flux.png" alt="Dev 50 Steps"> |
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</td> |
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<td> |
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<img src="./assets/flux_4.png" alt="Dev 4 Steps"> |
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</td> |
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<td> |
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<img src="./assets/merged_flux.png" alt="Dev + Schnell 4 Steps"> |
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</td> |
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</tr> |
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</tbody> |
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</table> |
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## Sub-memory-efficient merging code |
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```python |
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from diffusers import FluxTransformer2DModel |
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from huggingface_hub import snapshot_download |
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from accelerate import init_empty_weights |
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from diffusers.models.model_loading_utils import load_model_dict_into_meta |
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import safetensors.torch |
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import glob |
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import torch |
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with init_empty_weights(): |
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config = FluxTransformer2DModel.load_config("black-forest-labs/FLUX.1-dev", subfolder="transformer") |
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model = FluxTransformer2DModel.from_config(config) |
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dev_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", allow_patterns="transformer/*") |
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schnell_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-schnell", allow_patterns="transformer/*") |
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dev_shards = sorted(glob.glob(f"{dev_ckpt}/transformer/*.safetensors")) |
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schnell_shards = sorted(glob.glob(f"{schnell_ckpt}/transformer/*.safetensors")) |
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merged_state_dict = {} |
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guidance_state_dict = {} |
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for i in range(len((dev_shards))): |
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state_dict_dev_temp = safetensors.torch.load_file(dev_shards[i]) |
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state_dict_schnell_temp = safetensors.torch.load_file(schnell_shards[i]) |
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keys = list(state_dict_dev_temp.keys()) |
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for k in keys: |
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if "guidance" not in k: |
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merged_state_dict[k] = (state_dict_dev_temp.pop(k) + state_dict_schnell_temp.pop(k)) / 2 |
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else: |
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guidance_state_dict[k] = state_dict_dev_temp.pop(k) |
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if len(state_dict_dev_temp) > 0: |
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raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.") |
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if len(state_dict_schnell_temp) > 0: |
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raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.") |
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merged_state_dict.update(guidance_state_dict) |
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load_model_dict_into_meta(model, merged_state_dict) |
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model.to(torch.bfloat16).save_pretrained("merged-flux") |
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``` |
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## Inference code |
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```python |
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from diffusers import FluxPipeline, FluxTransformer2DModel |
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import torch |
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pipeline = FluxPipeline.from_pretrained( |
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"sayakpaul/FLUX.1-merged", transformer=transformer, torch_dtype=torch.bfloat16 |
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).to("cuda") |
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image = pipeline( |
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prompt="a tiny astronaut hatching from an egg on the moon", |
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guidance_scale=3.5, |
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num_inference_steps=4, |
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height=880, |
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width=1184, |
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max_sequence_length=512, |
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generator=torch.manual_seed(0), |
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).images[0] |
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image.save("merged_flux.png") |
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``` |
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## Documentation |
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* https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux |
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* https://huggingface.co/docs/diffusers/main/en/api/models/flux_transformer |
