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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ComfyUI_00381_.webp filter=lfs diff=lfs merge=lfs -text
+comfy[[:space:]]workflow.webp filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,3 +1,50 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- text-to-image
+---
+# AuraFlow v0.3
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6380ebb8471a4550ff255c62/jMkLXPFVNGdUb7P4nNTqX.png)
+
+
+AuraFlow v0.3 is the fully open-sourced largest flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, [AuraFlow-v0.2](https://huggingface.co/fal/AuraFlow-v0.2).
+
+Compared to AuraFlow-v0.2, the model is fine-tuned on more aesthetic datasets and now supports various aspect ratio, (now width and height up to 1536 pixels).
+
+This model achieves state-of-the-art results on GenEval. Read our [blog post](https://blog.fal.ai/auraflow/) for more technical details. You can also check out the comparison with other models on this gallery [page](https://cloneofsimo.github.io/compare_aura_sd3/).
+
+The model is currently in beta. We are working on improving it and the community's feedback is important.
+Join [fal's Discord](https://discord.gg/fal-ai) to give us feedback and stay in touch with the model development.
+
+Credits: A huge thank you to [@cloneofsimo](https://twitter.com/cloneofsimo) and [@isidentical](https://twitter.com/isidentical) for bringing this project to life. It's incredible what two cracked engineers can achieve in
+such a short period of time. We also extend our gratitude to the incredible researchers whose prior work laid the foundation for our efforts.
+
+## Usage
+
+```bash
+$ pip install transformers accelerate protobuf sentencepiece
+$ pip install git+https://github.com/huggingface/diffusers.git
+```
+
+```python
+from diffusers import AuraFlowPipeline
+import torch
+
+pipeline = AuraFlowPipeline.from_pretrained(
+    "fal/AuraFlow-v0.2",
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+image = pipeline(
+    prompt="close-up portrait of a majestic iguana with vibrant blue-green scales, piercing amber eyes, and orange spiky crest. Intricate textures and details visible on scaly skin. Wrapped in dark hood, giving regal appearance. Dramatic lighting against black background. Hyper-realistic, high-resolution image showcasing the reptile's expressive features and coloration.",
+    height=1024,
+    width=1024,
+    num_inference_steps=50, 
+    generator=torch.Generator().manual_seed(666),
+    guidance_scale=3.5,
+).images[0]
+
+image.save("output.png")
+```

aura_flow_0.3.safetensors

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comfy_workflow.json

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model_index.json

@@ -0,0 +1,25 @@
+{
+  "_class_name": "AuraFlowPipeline",
+  "_diffusers_version": "0.31.0.dev0",
+  "_name_or_path": "fal/AuraFlow-v0.3",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "UMT5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "LlamaTokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "AuraFlowTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,6 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.30.0.dev0",
+  "num_train_timesteps": 1000,
+  "shift": 1.73
+}

text_encoder/config.json

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+  "_name_or_path": "/raid/.cache/huggingface/models--fal--AuraFlow/snapshots/edf69bec4c8c57f5278a655aaca3ceb60d82c0b4/text_encoder",
+  "architectures": [
+    "UMT5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
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+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "scalable_attention": true,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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text_encoder/model.safetensors

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tokenizer/added_tokens.json

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+}

tokenizer/special_tokens_map.json

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+{
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tokenizer/tokenizer.model

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+size 499723

tokenizer/tokenizer_config.json

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vae/config.json

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+  "latents_mean": null,
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+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.13025,
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+  "up_block_types": [
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+  "use_post_quant_conv": true,
+  "use_quant_conv": true
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