Update Readme.md (#1)

- Update Readme.md (38139e0fb57ea3e9d4ae79433f160a70664d8e38)


Co-authored-by: xrp <xuruipeng8641@users.noreply.huggingface.co>

README.md

@@ -1,198 +1,81 @@
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 library_name: diffusers
+base_model:
+- madebyollin/taesd
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
 
 
-## Model Details
+# 🍰 Hybrid-sd-tinyvae for Stable Diffusion
 
-### Model Description
+[Hybrid-sd-tinyvae](https://huggingface.co/cqyan/hybrid-sd-tinyvae) is very tiny autoencoder which uses the same "latent API" as [SD](stabilityai/stable-diffusion-2-1-base).
+Hybrid-sd-tinyvae is a finetuned model based on the excellent work on [TAESD](https://github.com/madebyollin/taesd). In general, we mainly fix the low-saturation problem encountering in SD1.5 base model, by which we strengthening the saturation and contrast of images to deliver more clarity and colorfulness. 
+The model is useful for real-time previewing of the SD1.5 generation process. It saves 11x decoder inference time (16.38ms,fp16,V100) compared to using the SD1.5 decoder (186.6ms,fp16,V100), and you are very welcome to try it !!!!!! 
 
-<!-- Provide a longer summary of what this model is. -->
+T2I Comparison using one A100 GPU, The image order from left to right :  [SD1.5](stabilityai/stable-diffusion-2-1-base)  ->  [TAESD](https://github.com/madebyollin/taesd)  ->  [Hybrid-sd-tinyvae](https://huggingface.co/cqyan/hybrid-sd-tinyvae)
 
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/egu19RWslTCcsnCAjzC1d.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/1angYeWCFudrY34qMdogw.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/664afcc45fdb7108205a15c3/-cyqzkeNyjlZ6l3RvtqSz.png)
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+This repo contains `.safetensors` versions of the Hybrid-sd-tinyvae weights.
+For SDXL, use [Hybrid-sd-tinyvae-xl](https://huggingface.co/cqyan/hybrid-sd-tinyvae-xl) instead (the SD and SDXL VAEs are incompatible).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
 
-### Downstream Use [optional]
+## Using in 🧨 diffusers
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```python
+import torch
+from diffusers.models import AutoencoderTiny
+from diffusers import DiffusionPipeline
 
-[More Information Needed]
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16
+)
 
-### Out-of-Scope Use
+vae = AutoencoderTiny.from_pretrained('cqyan/hybrid-sd-tinyvae', torch_dtype=torch.float16)
+pipe.vae = vae
+pipe = pipe.to("cuda")
+prompt = "A warm and loving family portrait, highly detailed, hyper-realistic, 8k resolution, photorealistic, soft and natural lighting"
+image = pipe(prompt, num_inference_steps=25).images[0]
+image.save("family.png")
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## How to Get Started with the Model
 
-Use the code below to get started with the model.
 
-[More Information Needed]
 
-## Training Details
 
-### Training Data
 
-<!-- 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. -->
 
-[More Information Needed]
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
 
-[More Information Needed]
 
 
-#### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
 
-## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
 
-#### Testing Data
 
-<!-- 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 -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **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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-## Citation [optional]
-
-<!-- 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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-**APA:**
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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 [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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