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LICENSE

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+Stable Cascade NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT	
+Dated: February 10, 2024
+
+“AUP” means the Stability AI Acceptable Use Policy available at https://stability.ai/use-policy, as may be updated from time to time.
+
+"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Software Products set forth herein.
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README.md

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+---
+pipeline_tag: text-to-image
+license: other
+license_name: stable-cascade-nc-community
+license_link: LICENSE
+---
+
+# Stable Cascade Prior
+
+<!-- Provide a quick summary of what the model is/does. -->
+<img src="figures/collage_1.jpg" width="800">
+
+This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main 
+difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this 
+important? The smaller the latent space, the **faster** you can run inference and the **cheaper** the training becomes. 
+How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being 
+encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 
+1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the 
+highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable 
+Diffusion 1.5. <br> <br>
+Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions
+like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.
+
+## Model Details
+
+### Model Description
+
+Stable Cascade is a diffusion model trained to generate images given a text prompt.
+
+- **Developed by:** Stability AI
+- **Funded by:** Stability AI
+- **Model type:** Generative text-to-image model
+
+### Model Sources
+
+For research purposes, we recommend our `StableCascade` Github repository (https://github.com/Stability-AI/StableCascade).
+
+- **Repository:** https://github.com/Stability-AI/StableCascade
+- **Paper:** https://openreview.net/forum?id=gU58d5QeGv
+
+### Model Overview
+Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,
+hence the name "Stable Cascade".
+Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. 
+However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a 
+spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves 
+a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the 
+image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible 
+for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.
+
+<img src="figures/model-overview.jpg" width="600">
+
+For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with 
+a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was 
+put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve 
+great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the 
+best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to 
+its small size.
+
+## Evaluation
+<img height="300" src="figures/comparison.png"/>
+According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all 
+comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and 
+aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference 
+steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).
+
+## Code Example
+```shell
+#install `diffusers` from this branch while the PR is WIP
+pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3
+```
+
+```python
+import torch
+from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
+
+device = "cuda"
+dtype = torch.bfloat16
+num_images_per_prompt = 2
+
+prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype).to(device)
+decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=dtype).to(device)
+
+prompt = "Anthropomorphic cat dressed as a pilot"
+negative_prompt = ""
+
+prior_output = prior_pipeline(
+    prompt=caption,
+    height=1024,
+    width=1024,
+    negative_prompt=negative_prompt,
+    guidance_scale=4.0,
+    num_images_per_prompt=num_images_per_prompt,
+)
+decoder_output = decoder_pipeline(
+    image_embeddings=prior_output.image_embeddings,
+    prompt=caption,
+    negative_prompt=negative_prompt,
+    guidance_scale=0.0,
+    output_type="pil",
+).images
+```
+
+## Uses
+
+### Direct Use
+
+The model is intended for research purposes for now. Possible research areas and tasks include
+
+- Research on generative models.
+- Safe deployment of models which have the potential to generate harmful content.
+- Probing and understanding the limitations and biases of generative models.
+- Generation of artworks and use in design and other artistic processes.
+- Applications in educational or creative tools.
+
+Excluded uses are described below.
+
+### Out-of-Scope Use
+
+The model was not trained to be factual or true representations of people or events, 
+and therefore using the model to generate such content is out-of-scope for the abilities of this model.
+The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy).
+
+## Limitations and Bias
+
+### Limitations
+- Faces and people in general may not be generated properly.
+- The autoencoding part of the model is lossy.
+
+
+### Recommendations
+
+The model is intended for research purposes only.
+
+## How to Get Started with the Model
+
+Check out https://github.com/Stability-AI/StableCascade

feature_extractor/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

image_encoder/config.json

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+{
+  "_name_or_path": "StableCascade-prior/image_encoder",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.0.dev0"
+}

image_encoder/model.safetensors

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model_index.json

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+{
+  "_class_name": "StableCascadePriorPipeline",
+  "_diffusers_version": "0.26.0.dev0",
+  "_name_or_path": "StableCascade-prior/",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "prior": [
+    "stable_cascade",
+    "StableCascadeUnet"
+  ],
+  "resolution_multiple": 42.67,
+  "scheduler": [
+    "diffusers",
+    "DDPMWuerstchenScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizerFast"
+  ]
+}

prior/config.json

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+{
+  "_class_name": "StableCascadeUnet",
+  "_diffusers_version": "0.26.0.dev0",
+  "_name_or_path": "StableCascade-prior/prior",
+  "block_repeat": [
+    [
+      1,
+      1
+    ],
+    [
+      1,
+      1
+    ]
+  ],
+  "blocks": [
+    [
+      8,
+      24
+    ],
+    [
+      24,
+      8
+    ]
+  ],
+  "c_clip_img": 768,
+  "c_clip_seq": 4,
+  "c_clip_text": 1280,
+  "c_clip_text_pooled": 1280,
+  "c_cond": 2048,
+  "c_effnet": null,
+  "c_hidden": [
+    2048,
+    2048
+  ],
+  "c_in": 16,
+  "c_out": 16,
+  "c_pixels": null,
+  "c_r": 64,
+  "dropout": [
+    0.1,
+    0.1
+  ],
+  "kernel_size": 3,
+  "level_config": [
+    "CTA",
+    "CTA"
+  ],
+  "nhead": [
+    32,
+    32
+  ],
+  "patch_size": 1,
+  "self_attn": true,
+  "switch_level": [
+    false
+  ],
+  "t_conds": [
+    "sca",
+    "crp"
+  ]
+}

prior/diffusion_pytorch_model.safetensors

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scheduler/scheduler_config.json

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+{
+  "_class_name": "DDPMWuerstchenScheduler",
+  "_diffusers_version": "0.26.0.dev0",
+  "s": 0.008,
+  "scaler": 1.0
+}

text_encoder/config.json

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+{
+  "_name_or_path": "StableCascade-prior/text_encoder",
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 49406,
+  "dropout": 0.0,
+  "eos_token_id": 49407,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.0.dev0",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

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tokenizer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}