Initialize unconditional model pipeline.

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+val_imgs_grid.png filter=lfs diff=lfs merge=lfs -text

README.md

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+
+---
+license: creativeml-openrail-m
+base_model: stabilityai/stable-diffusion-2-base
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- diffusers
+inference: true
+---
+
+# Unconditioned stable diffusion finetuning - uncond_sd2-base
+
+This pipeline was finetuned from **stabilityai/stable-diffusion-2-base**
+for brain image generation.
+Below are some example images generated with the finetuned pipeline: 
+
+![val_imgs_grid](./val_imgs_grid.png)
+
+
+## Pipeline usage
+
+You can use the pipeline like so:
+
+```python
+from diffusers import StableDiffusionUnconditionalPipeline
+import torch
+
+pipeline = StableDiffusionUnconditionalPipeline.from_pretrained("uncond_sd2-base", torch_dtype=torch.float32)
+image = pipeline(1).images[0]
+image.save("brain_image.png")
+```
+
+## Training info
+For training info, refer the model card for the parent conditional model: stabilityai/stable-diffusion-2-base.

model_index.json

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+{
+  "_class_name": "StableDiffusionUnconditionalPipeline",
+  "_diffusers_version": "0.26.0.dev0",
+  "requires_safety_checker": false,
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.26.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

unet/config.json

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+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.26.0.dev0",
+  "act_fn": "silu",
+  "add_mid_block_attention": true,
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "down_block_types": [
+    "SelfAttnDownBlock2D",
+    "SelfAttnDownBlock2D",
+    "SelfAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "downsample_type": "conv",
+  "dropout": 0.0,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DSelfAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "num_train_timesteps": null,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 64,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "SelfAttnUpBlock2D",
+    "SelfAttnUpBlock2D",
+    "SelfAttnUpBlock2D"
+  ],
+  "upcast_attention": false,
+  "upsample_type": "conv",
+  "use_linear_projection": true,
+  "use_transformer_attentions": true
+}

unet/diffusion_pytorch_model.safetensors

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vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.26.0.dev0",
+  "_name_or_path": "stabilityai/stable-diffusion-2-base",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.safetensors

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