upload

README.md

@@ -1,3 +1,56 @@
----
-license: openrail
----
+---
+license: openrail++
+tags:
+- text-to-image
+- PixArt-Σ
+---
+THIS IS A REDISTRIBUTION OF PIXART-Σ-XL-256x256
+
+### 🧨 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(
+    "dattrong/pixart-sigma-512", 
+    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()
+```

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
+}

text_encoder/config.json

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+{
+  "_name_or_path": "data/t5_ckpts",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "d_ff": 10240,
+  "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",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0",
+  "use_cache": true,
+  "vocab_size": 32128
+}

text_encoder/model-00001-of-00002.safetensors

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text_encoder/model-00002-of-00002.safetensors

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text_encoder/model.safetensors.index.json

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