Update README.md

README.md

@@ -1,62 +1,83 @@
 ---
 library_name: diffusers
+license: apache-2.0
+datasets:
+- common-canvas/commoncatalog-cc-by
+- alfredplpl/commoncatalog-cc-by-recap
+language:
+- en
 ---
 
-# Model Card for Model ID
+# CommonArt-PoC
 
-<!-- Provide a quick summary of what the model is/does. -->
+CommonArt is a text-to-image generation model with authorized images only.
+The architecture is based on DiT that is using by Stable Diffusion 3 and Sora.
 
+# Usage
 
+You can use this model by diffusers library.
 
-## Model Details
+```python
+import torch
+from diffusers import Transformer2DModel, PixArtSigmaPipeline
 
-### Model Description
+device = "cpu"
+weight_dtype = torch.float32
 
-<!-- Provide a longer summary of what this model is. -->
+transformer = Transformer2DModel.from_pretrained(
+    "alfredplpl/CommonArt-PoC", 
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+)
 
-This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
+pipe = PixArtSigmaPipeline.from_pretrained(
+    "PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
+    transformer=transformer,
+    torch_dtype=weight_dtype,
+    use_safetensors=True,
+)
 
-- **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]
+pipe.to(device)
 
-### Model Sources [optional]
+prompt = " A picturesque photograph of a serene coastline, capturing the tranquility of a sunrise over the ocean. The image shows a wide expanse of gently rolling sandy beach, with clear, turquoise water stretching into the horizon. Seashells and pebbles are scattered along the shore, and the sun's rays create a golden hue on the water's surface. The distant outline of a lighthouse can be seen, adding to the quaint charm of the scene. The sky is painted with soft pastel colors of dawn, gradually transitioning from pink to blue, creating a sense of peacefulness and beauty."
+image = pipe(prompt,guidance_scale=4.5,max_squence_length=512).images[0]
+image.save("beach.png")
+```
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Model Details
 
-## Uses
+### Model Description
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- **Developed by:** alfredplpl
+- **Funded by [optional]:** alfredplpl
+- **Shared by [optional]:** alfredplpl
+- **Model type:** Diffusion transformer
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
 
-### Direct Use
+### Model Sources 
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **Repository:** [Pixart-Sigma](https://github.com/PixArt-alpha/PixArt-sigma)
+- **Paper:** [PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation](https://arxiv.org/abs/2403.04692)
 
-[More Information Needed]
+## Uses
 
-### Downstream Use [optional]
+- Any purpose
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Direct Use
 
-[More Information Needed]
+- To develop commercial text-to-image generation.
+- To research non-commercial text-to-image generation.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- To generate misinformation.
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 [More Information Needed]
 
@@ -68,131 +89,99 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+You use
 
 ## 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. -->
+I used these dataset to train the transformer.
 
-[More Information Needed]
+- CommonCatalog CC BY
+- CommonCatalog CC BY
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### 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. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- **Training regime:**
+```bash
+_base_ = ['../PixArt_xl2_internal.py']
+data_root = "/mnt/my_raid/pixart"
+image_list_json = ['data_info.json']
+
+data = dict(
+    type='InternalDataSigma', root='InternData', image_list_json=image_list_json, transform='default_train',
+    load_vae_feat=False, load_t5_feat=False,
+)
+image_size = 256
+
+# model setting
+model = 'PixArt_XL_2'
+mixed_precision = 'fp16'  # ['fp16', 'fp32', 'bf16']
+fp32_attention = True
+#load_from = "/mnt/my_raid/pixart/working/checkpoints/epoch_1_step_17500.pth"  # https://huggingface.co/PixArt-alpha/PixArt-Sigma
+#resume_from = dict(checkpoint="/mnt/my_raid/pixart/working/checkpoints/epoch_37_step_62039.pth", load_ema=False, resume_optimizer=True, resume_lr_scheduler=True)
+vae_pretrained = "output/pretrained_models/pixart_sigma_sdxlvae_T5_diffusers/vae"  # sdxl vae
+multi_scale = False  # if use multiscale dataset model training
+pe_interpolation = 0.5
+
+# training setting
+num_workers = 10
+train_batch_size = 64  # 64 as default
+num_epochs = 200  # 3
+gradient_accumulation_steps = 1
+grad_checkpointing = True
+gradient_clip = 0.2
+optimizer = dict(type='CAMEWrapper', lr=2e-5, weight_decay=0.0, betas=(0.9, 0.999, 0.9999), eps=(1e-30, 1e-16))
+lr_schedule_args = dict(num_warmup_steps=1000)
+
+#visualize=True
+#train_sampling_steps = 3
+#eval_sampling_steps = 3
+log_interval = 20
+save_model_epochs = 1
+#save_model_steps = 2500
+work_dir = 'output/debug'
+
+# pixart-sigma
+scale_factor = 0.13025
+real_prompt_ratio = 0.5
+model_max_length = 512
+class_dropout_prob = 0.1
+
+```
 
 ## 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).
-
-- **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]
+- **Hardware Type:** A6000x2
+- **Hours used:** 1000
+- **Compute Region:** Japan
+- **Carbon Emitted:** Not so much
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Diffusion Transformer
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Desktop PC
 
 #### Hardware
 
-[More Information Needed]
+A6000x2
 
 #### Software
 
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+[Pixart-Sigma repository](https://github.com/PixArt-alpha/PixArt-sigma)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+alfredplpl