Duplicate from PixArt-alpha/PixArt-Sigma-XL-2-1024-MS

Co-authored-by: Chen <[email protected]>

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README.md

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+---
+license: openrail++
+tags:
+- text-to-image
+- PixArt-Σ
+---
+
+<p align="center">
+  <img src="asset/logo-sigma.png"  height=120>
+</p>
+
+<div style="display:flex;justify-content: center">
+  <a href="https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma"><img src="https://img.shields.io/static/v1?label=Demo&message=Huggingface&color=yellow"></a> &ensp;
+  <a href="https://pixart-alpha.github.io/PixArt-sigma-project/"><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/2403.04692"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv&color=red&logo=arxiv"></a> &ensp;
+  <a href="https://discord.gg/rde6eaE5Ta"><img src="https://img.shields.io/static/v1?label=Discuss&message=Discord&color=purple&logo=discord"></a> &ensp;
+</div>
+
+# 🐱 PixArt-Σ Model Card
+![row01](asset/4K_image.jpg)
+
+## Model
+![pipeline](asset/model.png)
+
+[PixArt-Σ](https://arxiv.org/abs/2403.04692) consists of pure transformer blocks for latent diffusion: 
+It can directly generate 1024px, 2K and 4K images from text prompts within a single sampling process.
+
+Source code is available at https://github.com/PixArt-alpha/PixArt-sigma.
+
+### Model Description
+
+- **Developed by:** PixArt-Σ
+- **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-sigma) and the [PixArt-Σ report on arXiv](https://arxiv.org/abs/2403.04692).
+
+### Model Sources
+
+For research purposes, we recommend our `generative-models` Github repository (https://github.com/PixArt-alpha/PixArt-sigma), 
+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-Sigma) provides free PixArt-Σ inference.
+- **Repository:** https://github.com/PixArt-alpha/PixArt-sigma
+- **Demo:** https://huggingface.co/spaces/PixArt-alpha/PixArt-Sigma
+
+### 🧨 Diffusers 
+> [!IMPORTANT]  
+> Make sure to upgrade diffusers to >= 0.28.0:
+> ```bash
+> pip install -U diffusers --upgrade
+> ```
+> In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`:
+> ```
+> pip install transformers accelerate safetensors sentencepiece
+> ```
+> For `diffusers<0.28.0`, check this [script](https://github.com/PixArt-alpha/PixArt-sigma#2-integration-in-diffusers) for help.
+
+To just use the base model, you can run:
+
+```python
+import torch
+from diffusers import Transformer2DModel, PixArtSigmaPipeline
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+weight_dtype = torch.float16
+
+pipe = PixArtSigmaPipeline.from_pretrained(
+    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", 
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+)
+pipe.to(device)
+
+# Enable memory optimizations.
+# pipe.enable_model_cpu_offload()
+
+prompt = "A small cactus with a happy face in the Sahara desert."
+image = pipe(prompt).images[0]
+image.save("./catcus.png")
+```
+
+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-Σ with `diffusers`, please have a look at [the PixArt-Σ Docs](https://huggingface.co/docs/diffusers/main/en/api/pipelines/pixart_sigma.md).
+
+## 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.

model_index.json

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+{
+  "_class_name": "PixArtSigmaPipeline",
+  "_diffusers_version": "0.28.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "DPMSolverMultistepScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "Transformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+  "_class_name": "DPMSolverMultistepScheduler",
+  "_diffusers_version": "0.22.0.dev0",
+  "algorithm_type": "dpmsolver++",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "dynamic_thresholding_ratio": 0.995,
+  "euler_at_final": false,
+  "lambda_min_clipped": -Infinity,
+  "lower_order_final": true,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "solver_order": 2,
+  "solver_type": "midpoint",
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "linspace",
+  "trained_betas": null,
+  "use_karras_sigmas": false,
+  "use_lu_lambdas": false,
+  "variance_type": null
+}

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+  ],
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+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
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+  "num_heads": 64,
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+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0",
+  "use_cache": true,
+  "vocab_size": 32128
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