Upload model
Browse files- README.md +200 -0
- config.json +115 -0
- diffusion_pytorch_model.safetensors +3 -0
README.md
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---
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library_name: diffusers
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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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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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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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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config.json
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{
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"_class_name": "ForwardModelUNet2DConditionModel",
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"_diffusers_version": "0.26.3",
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"_name_or_path": "ckpts/unet_lgm",
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"beta_end": 0.012,
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"beta_schedule": "scaled_linear",
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"beta_start": 0.00085,
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"clip_sample": false,
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"clip_sample_range": 1.0,
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"dynamic_thresholding_ratio": 0.995,
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"lgm_cam_radius": 1.5,
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"lgm_down_attention": [
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false,
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false,
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false,
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true,
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true,
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true
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],
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"lgm_down_channels": [
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64,
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128,
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256,
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512,
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1024,
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1024
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],
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"lgm_fovy": 49.1343,
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"lgm_input_size": 256,
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"lgm_mid_attention": true,
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"lgm_output_size": 512,
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"lgm_splat_size": 128,
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"lgm_up_attention": [
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true,
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true,
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true,
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false,
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false
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],
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"lgm_up_channels": [
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1024,
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1024,
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512,
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256,
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128
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],
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"lgm_zfar": 2.5,
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"lgm_znear": 0.5,
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"num_train_timesteps": 1000,
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"prediction_type": "epsilon",
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"rescale_betas_zero_snr": false,
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"sample_max_value": 1.0,
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"set_alpha_to_one": false,
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"thresholding": false,
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"unet_adm_in_channels": null,
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"unet_attention_resolutions": [
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4,
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2,
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1
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],
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"unet_camera_dim": 16,
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"unet_channel_mult": [
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1,
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2,
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4,
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4
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],
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"unet_context_dim": 1024,
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"unet_conv_resample": true,
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"unet_dims": 2,
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"unet_dropout": 0,
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"unet_image_size": 32,
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"unet_in_channels": 4,
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"unet_ip_dim": 16,
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"unet_ip_weight": 1.0,
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"unet_model_channels": 320,
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"unet_n_embed": null,
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"unet_num_attention_blocks": null,
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"unet_num_classes": null,
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"unet_num_head_channels": 64,
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"unet_num_heads": -1,
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"unet_num_heads_upsample": -1,
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"unet_num_res_blocks": 2,
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"unet_out_channels": 4,
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"unet_resblock_updown": false,
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"unet_transformer_depth": 1,
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"unet_use_scale_shift_norm": false,
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"vae_act_fn": "silu",
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"vae_block_out_channels": [
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128,
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256,
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512,
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512
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],
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"vae_down_block_types": [
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D",
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"DownEncoderBlock2D"
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],
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"vae_force_upcast": true,
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"vae_in_channels": 3,
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"vae_latent_channels": 4,
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"vae_layers_per_block": 2,
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"vae_norm_num_groups": 32,
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"vae_out_channels": 3,
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"vae_sample_size": 512,
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"vae_scaling_factor": 0.18215,
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"vae_up_block_types": [
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D",
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"UpDecoderBlock2D"
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]
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}
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diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9ee864adadd756f6ae963eab1a8e1f993ce6cde67685bc09d243571a72250ddf
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size 2880905326
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