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README.md
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library_name: diffusers
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---
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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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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base_model:
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- black-forest-labs/FLUX.1-dev
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- black-forest-labs/FLUX.1-schnell
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- ByteDance/Hyper-SD
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base_model_relation: merge
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# FLUX.1-merged_uncensored
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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) originally provided by [@sayakpaul](https://huggingface.co/sayakpaul/FLUX.1-merged).
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The base model was then fused with a selection of LoRAs, *some of which are NSFW in nature*. Please use responsibily. Please be aware of the licenses of both the models before using the params commercially.
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This model was created as part of the ongoing [OpenSight project](https://huggingface.co/spaces/aiwithoutborders-xyz/OpenSight-Deepfake-Detection-Models-Playground), mostly for dataset generation and evaluation purposes.
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## The following context provided by sayakpaul, of the original merged model.
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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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