initial commit

README.md

@@ -1,3 +1,22 @@
 ---
 license: creativeml-openrail-m
+language:
+- en
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+inference:
+  parameters:
+    num_inference_steps: 50
+    guidance_scale: 5.0
+    eta: 1.0
 ---
+
+# ddpo-aesthetic
+
+This model was finetuned from [Stable Diffusion v1-5](https:/runwayml/stable-diffusion-v1-5) using [DDPO](https://arxiv.org/abs/2305.13301) and a reward function encouraging images that are _not_ JPEG-compressible. See [the project website](https://rl-diffusion.github.io/) for more details.
+
+The model was finetuned for 20 iterations with a batch size of 256 samples per iteration. During finetuning, it was prompted with all of the animals in the [Imagenet-1000](https://deeplearning.cms.waikato.ac.nz/user-guide/class-maps/IMAGENET/) categories (the first 398 categories), but exhibits some generalization to other prompts.

feature_extractor/preprocessor_config.json

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+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPFeatureExtractor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

model_index.json

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+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.16.1",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

safety_checker/config.json

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+  "_name_or_path": "CompVis/stable-diffusion-safety-checker",
+  "architectures": [
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+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
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+}

safety_checker/pytorch_model.bin

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scheduler/scheduler_config.json

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+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.16.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": true,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "steps_offset": 1,
+  "thresholding": false,
+  "trained_betas": null
+}

text_encoder/config.json

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+{
+  "_name_or_path": "flax_tmp/text_encoder",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
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+  "projection_dim": 768,
+  "torch_dtype": "float32",
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text_encoder/pytorch_model.bin

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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unet/config.json

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+  "_diffusers_version": "0.16.1",
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vae/config.json

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+  "_diffusers_version": "0.16.1",
+  "_name_or_path": "flax_tmp/vae",
+  "act_fn": "silu",
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+  "up_block_types": [
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+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

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