John M
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Browse files- README.md +42 -1
- model_index.json +33 -0
- scheduler/scheduler_config.json +18 -0
- text_encoder/config.json +25 -0
- text_encoder/model.safetensors +3 -0
- text_encoder_2/config.json +25 -0
- text_encoder_2/model.safetensors +3 -0
- tokenizer/merges.txt +0 -0
- tokenizer/special_tokens_map.json +24 -0
- tokenizer/tokenizer_config.json +33 -0
- tokenizer/vocab.json +0 -0
- tokenizer_2/merges.txt +0 -0
- tokenizer_2/special_tokens_map.json +24 -0
- tokenizer_2/tokenizer_config.json +33 -0
- tokenizer_2/vocab.json +0 -0
- unet/config.json +70 -0
- unet/diffusion_pytorch_model.safetensors +3 -0
- vae/config.json +32 -0
- vae/diffusion_pytorch_model.safetensors +3 -0
README.md
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---
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-
license:
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---
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---
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license: apache-2.0
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---
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![image/gif](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/ux_sZKB9snVPsKRT1TzfG.gif)
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## Try Hotshot-XL yourself here: https://www.hotshot.co
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Hotshot-XL is an AI text-to-GIF model trained to work alongside [Stable Diffusion XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
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Hotshot-XL can generate GIFs with any fine-tuned SDXL model. This means two things:
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1. You’ll be able to make GIFs with any existing or newly fine-tuned SDXL model you may want to use.
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2. If you'd like to make GIFs of personalized subjects, you can load your own SDXL based LORAs, and not have to worry about fine-tuning Hotshot-XL. This is awesome because it’s usually much easier to find suitable images for training data than it is to find videos. It also hopefully fits into everyone's existing LORA usage/workflows :) See more [here](https://github.com/naturalsynthetics/hotshot-xl-private/blob/main/README.md#text-to-gif-with-personalized-loras).
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Hotshot-XL is compatible with SDXL ControlNet to make GIFs in the composition/layout you’d like. See [here](https://github.com/hotshotco/hotshot-xl-private/blob/main/README.md#text-to-gif-with-controlnet) for more info.
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Hotshot-XL was trained to generate 1 second GIFs at 8 FPS.
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Hotshot-XL was trained on various aspect ratios. For best results with the base Hotshot-XL model, we recommend using it with an SDXL model that has been fine-tuned with 512x512 images. You can find an SDXL model we fine-tuned for 512x512 resolutions [here](https://github.com/naturalsynthetics/hotshot-xl-private/blob/main/README.md#text-to-gif-with-personalized-loras).
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![image/gif](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/XXgnk14nIasPdkvkPlDzn.gif)
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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/637a6daf7ce76c3b83497ea2/6OknWOlsl9Zs_esGtPTlZ.jpeg)
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Source code is available at https://github.com/hotshotco/hotshot-xl.
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# Model Description
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- **Developed by**: Natural Synthetics Inc.
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- **Model type**: Diffusion-based text-to-GIF generative model
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- **License**: Apache 2.0 License
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- **Model Description**: This is a model that can be used to generate and modify GIFs based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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- **Resources for more information**: Check out our [GitHub Repository](https://github.com/hotshotco/hotshot-xl).
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# Limitations and Bias
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## Limitations
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- The model does not achieve perfect photorealism
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- The model cannot render legible text
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- The model struggles with more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
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- Faces and people in general may not be generated properly.
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## Bias
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While the capabilities of video generation models are impressive, they can also reinforce or exacerbate social biases.
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model_index.json
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{
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"_class_name": "HotshotXLPipeline",
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"_diffusers_version": "0.21.4",
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"force_zeros_for_empty_prompt": true,
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"scheduler": [
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"diffusers",
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"EulerAncestralDiscreteScheduler"
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],
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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],
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"text_encoder_2": [
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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],
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"tokenizer_2": [
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"transformers",
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"CLIPTokenizer"
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],
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"unet": [
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"hotshot_xl",
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"UNet3DConditionModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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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": "EulerDiscreteScheduler",
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"_diffusers_version": "0.18.1",
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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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"interpolation_type": "linear",
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"num_train_timesteps": 1000,
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"prediction_type": "epsilon",
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"sample_max_value": 1.0,
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"set_alpha_to_one": false,
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"skip_prk_steps": true,
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"steps_offset": 1,
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"timestep_spacing": "leading",
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"trained_betas": null,
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"use_karras_sigmas": false
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}
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text_encoder/config.json
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{
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"_name_or_path": "/home/ubuntu/models/Hotshot-XL",
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"architectures": [
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"CLIPTextModel"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dropout": 0.0,
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"eos_token_id": 2,
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"hidden_act": "quick_gelu",
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"hidden_size": 768,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 77,
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"model_type": "clip_text_model",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"projection_dim": 768,
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"torch_dtype": "float32",
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"transformers_version": "4.33.3",
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"vocab_size": 49408
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}
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text_encoder/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5c3d6454dd2d23414b56aa1b5858a72487a656937847b6fea8d0606d7a42cdbc
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size 492265168
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text_encoder_2/config.json
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{
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"_name_or_path": "/home/ubuntu/models/Hotshot-XL",
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"architectures": [
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"CLIPTextModelWithProjection"
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],
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"model_type": "clip_text_model",
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}
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text_encoder_2/model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2778702264
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tokenizer/merges.txt
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tokenizer/special_tokens_map.json
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tokenizer/tokenizer_config.json
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tokenizer/vocab.json
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tokenizer_2/merges.txt
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tokenizer_2/special_tokens_map.json
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}
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tokenizer_2/tokenizer_config.json
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}
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}
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tokenizer_2/vocab.json
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unet/config.json
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vae/config.json
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@@ -0,0 +1,32 @@
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