Commit
•
21e2ae8
1
Parent(s):
24c0254
Upload DDPMPipeline
Browse files- README.md +198 -0
- model_index.json +12 -0
- scheduler/scheduler_config.json +19 -0
- unet/config.json +51 -0
- unet/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 pipeline 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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model_index.json
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{
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"_class_name": "DDPMPipeline",
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"_diffusers_version": "0.29.2",
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"scheduler": [
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"diffusers",
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"DDPMScheduler"
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],
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"unet": [
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"diffusers",
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"UNet2DModel"
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]
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}
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scheduler/scheduler_config.json
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{
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"_class_name": "DDPMScheduler",
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"_diffusers_version": "0.29.2",
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"beta_start": 0.0001,
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"clip_sample": true,
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"clip_sample_range": 1.0,
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"dynamic_thresholding_ratio": 0.995,
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"num_train_timesteps": 2000,
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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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"steps_offset": 0,
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"thresholding": false,
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"timestep_spacing": "leading",
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"trained_betas": null,
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"variance_type": "fixed_small"
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}
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unet/config.json
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{
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"_class_name": "UNet2DModel",
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"_diffusers_version": "0.29.2",
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"act_fn": "silu",
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"add_attention": true,
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"attention_head_dim": 8,
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"attn_norm_num_groups": null,
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"block_out_channels": [
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32,
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64,
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64,
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128,
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256
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],
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"center_input_sample": false,
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"class_embed_type": null,
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"down_block_types": [
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"DownBlock2D",
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"DownBlock2D",
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"DownBlock2D",
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"DownBlock2D",
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"AttnDownBlock2D"
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],
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"downsample_padding": 1,
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"downsample_type": "conv",
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"dropout": 0.0,
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"flip_sin_to_cos": true,
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"freq_shift": 0,
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"in_channels": 3,
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"layers_per_block": 2,
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"mid_block_scale_factor": 1,
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"norm_eps": 1e-05,
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"norm_num_groups": 32,
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"num_class_embeds": null,
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"num_train_timesteps": null,
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"out_channels": 3,
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"resnet_time_scale_shift": "default",
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"sample_size": [
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160,
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128
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],
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"time_embedding_type": "positional",
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"up_block_types": [
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"AttnUpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D"
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],
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"upsample_type": "conv"
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}
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unet/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:322c06bd7025cb9a90f23f8a470bbab58f367c084062f583a9e6ff9adf8d7787
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size 64432932
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