megactor-sigma alpha

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weights/PixArt_XL_2_512/README.md

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+---
+license: openrail++
+tags:
+- text-to-image
+- Pixart-α
+---
+
+<p align="center">
+  <img src="asset/logo.png"  height=120>
+</p>
+
+<div style="display:flex;justify-content: center">
+  <a href="https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha"><img src="https://img.shields.io/static/v1?label=Demo&message=Huggingface&color=yellow"></a> &ensp;
+  <a href="https://pixart-alpha.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp;
+  <a href="https://arxiv.org/abs/2310.00426"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a> &ensp;
+  <a href="https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing"><img src="https://img.shields.io/static/v1?label=Free%20Trial&message=Google%20Colab&logo=google&color=orange"></a> &ensp;
+  <a href="https://github.com/orgs/PixArt-alpha/discussions"><img src="https://img.shields.io/static/v1?label=Discussion&message=Github&color=green&logo=github"></a> &ensp;
+</div>
+
+# 🐱 Pixart-α Model Card
+![row01](asset/images/teaser.png)
+
+## Model
+![pipeline](asset/images/model.png)
+
+[Pixart-α](https://arxiv.org/abs/2310.00426)
+ consists of pure transformer blocks for latent diffusion: 
+It can directly generate 1024px images from text prompts within a single sampling process.
+
+Source code is available at https://github.com/PixArt-alpha/PixArt-alpha.
+
+### Model Description
+
+- **Developed by:** Pixart-α
+- **Model type:** Diffusion-Transformer-based text-to-image generative model
+- **
+License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
+- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. 
+It is a [Transformer Latent Diffusion Model](https://arxiv.org/abs/2310.00426) that uses one fixed, pretrained text encoders ([T5](
+https://huggingface.co/DeepFloyd/t5-v1_1-xxl))
+and one latent feature encoder ([VAE](https://arxiv.org/abs/2112.10752)).
+- **Resources for more information:** Check out our [GitHub Repository](https://github.com/PixArt-alpha/PixArt-alpha) and the [Pixart-α report on arXiv](https://arxiv.org/abs/2310.00426).
+
+### Model Sources
+
+For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-alpha), 
+which is more suitable for both training and inference and for which most advanced diffusion sampler like [SA-Solver](https://arxiv.org/abs/2309.05019) will be added over time.
+[Hugging Face](https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha) provides free Pixart-α inference.
+- **Repository:** https://github.com/PixArt-alpha/PixArt-alpha
+- **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-alpha
+
+# 🔥🔥🔥 Why PixArt-α? 
+## Training Efficiency
+PixArt-α only takes 10.8% of Stable Diffusion v1.5's training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%.
+![Training Efficiency.](asset/images/efficiency.svg)
+
+| Method    | Type | #Params | #Images | A100 GPU days |
+|-----------|------|---------|---------|---------------|
+| DALL·E    | Diff | 12.0B   | 1.54B   |               |
+| GLIDE     | Diff | 5.0B    | 5.94B   |               |
+| LDM       | Diff | 1.4B    | 0.27B   |               |
+| DALL·E 2  | Diff | 6.5B    | 5.63B   | 41,66         |
+| SDv1.5    | Diff | 0.9B    | 3.16B   | 6,250         |
+| GigaGAN   | GAN  | 0.9B    | 0.98B   | 4,783         |
+| Imagen    | Diff | 3.0B    | 15.36B  | 7,132         |
+| RAPHAEL   | Diff | 3.0B    | 5.0B    | 60,000        |
+| PixArt-α  | Diff | 0.6B    | 0.025B  | 675           |
+
+
+## Evaluation
+![comparison](asset/images/user-study.png)
+The chart above evaluates user preference for Pixart-α over SDXL 0.9, Stable Diffusion 2, DALLE-2 and DeepFloyd. 
+The Pixart-α base model performs comparable or even better than the existing state-of-the-art models.
+
+
+
+### 🧨 Diffusers 
+
+Make sure to upgrade diffusers to >= 0.22.0:
+```
+pip install -U diffusers --upgrade
+```
+
+In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`:
+```
+pip install transformers accelerate safetensors
+```
+
+To just use the base model, you can run:
+
+
+```py
+from diffusers import PixArtAlphaPipeline
+import torch
+
+pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-512x512", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+# if using torch < 2.0
+# pipe.enable_xformers_memory_efficient_attention()
+
+prompt = "An astronaut riding a green horse"
+images = pipe(prompt=prompt).images[0]
+```
+
+When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
+```py
+pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
+```
+
+If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
+instead of `.to("cuda")`:
+
+```diff
+- pipe.to("cuda")
++ pipe.enable_model_cpu_offload()
+```
+
+For more information on how to use Pixart-α with `diffusers`, please have a look at [the Pixart-α Docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pixart).
+
+### Free Google Colab
+You can use Google Colab to generate images from PixArt-α free of charge. Click [here](https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing) too try.
+
+## Uses
+
+### Direct Use
+
+The model is intended for research purposes only. Possible research areas and tasks include
+
+- Generation of artworks and use in design and other artistic processes.
+- Applications in educational or creative tools.
+- 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.
+
+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.
+
+## Limitations and Bias
+
+### Limitations
+
+
+- The model does not achieve perfect photorealism
+- The model cannot render legible text
+- 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”
+- fingers, .etc in general may not be generated properly.
+- The autoencoding part of the model is lossy.
+
+### Bias
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.

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