Juggernaut X-Hyper as fp16 diffusers tree (format conversion only)

README.md

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+---
+license: creativeml-openrail-m
+base_model: RunDiffusion/Juggernaut-X-Hyper
+tags:
+- text-to-image
+- stable-diffusion
+- stable-diffusion-xl
+- diffusers
+library_name: diffusers
+pipeline_tag: text-to-image
+---
+
+# Juggernaut X-Hyper — fp16 diffusers tree
+
+A **format conversion** of [RunDiffusion/Juggernaut-X-Hyper](https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper).
+The weights are RunDiffusion's; nothing has been retrained, merged, or fine-tuned.
+
+## Why this repo exists
+
+The upstream repo cannot be loaded by `diffusers`/MLX as published:
+
+- its diffusers tree stores weights as **PyTorch pickle** (`.bin`), not safetensors;
+- its tokenizers ship without the legacy CLIP pair (`vocab.json` / `merges.txt`)
+  that SDXL text-encoding pipelines expect.
+
+The only directly usable weights upstream are in the root single-file
+checkpoint, `JuggernautXRundiffusion_Hyper.safetensors`.
+
+## Changes made
+
+1. Loaded the upstream single-file checkpoint with
+   `StableDiffusionXLPipeline.from_single_file(...)` and re-saved it as a
+   standard fp16 diffusers tree (`safe_serialization=True`, `variant="fp16"`).
+2. Restored `vocab.json` and `merges.txt` for `tokenizer/` and `tokenizer_2/`
+   from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
+   SDXL fine-tunes do not retrain the CLIP tokenizers, so these files are
+   byte-identical across SDXL models (`vocab.json` 1,059,962 bytes,
+   `merges.txt` 524,619 bytes).
+
+No weight values were altered. The conversion script is
+[`sdxl_single_file_to_diffusers.py`](https://github.com/Gatcha-man/mindfire-image/blob/main/scripts/sdxl_single_file_to_diffusers.py).
+
+## Recommended settings
+
+Per the upstream model card:
+
+| Setting | Value |
+|---|---|
+| Steps | 4–8 (start at 6) |
+| CFG scale | 1.0–2.0 |
+| Sampler | DPM++ SDE or TCD |
+| Resolution | 1024×1024 (SDXL native) |
+
+Verified on an M-series Mac Studio via MLX: 6 steps, CFG 2.0, 1024×1024,
+~7.5s per image.
+
+## License
+
+**CreativeML Open RAIL-M**, inherited unchanged from the upstream model —
+see the [original model card](https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper)
+and the [license text](https://huggingface.co/spaces/CompVis/stable-diffusion-license).
+
+Upstream states: *"This model may not be deployed behind paid API services
+without explicit licensing."* Commercial licensing:
+[juggernaut@rundiffusion.com](mailto:juggernaut@rundiffusion.com).
+
+The RAIL-M use-based restrictions apply to this copy exactly as they apply to
+the original.
+
+## Attribution
+
+Juggernaut X-Hyper by **RunDiffusion / KandooAI**.
+Original: <https://huggingface.co/RunDiffusion/Juggernaut-X-Hyper>

model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.39.0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

scheduler/scheduler_config.json

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+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.39.0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "final_sigmas_type": "zero",
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "sigma_max": null,
+  "sigma_min": null,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "timestep_type": "discrete",
+  "trained_betas": null,
+  "use_beta_sigmas": false,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}

text_encoder/config.json

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+{
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "dtype": "float16",
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "transformers_version": "5.13.1",
+  "vocab_size": 49408
+}

text_encoder/model.fp16.safetensors

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text_encoder_2/config.json

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+{
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "dtype": "float16",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "transformers_version": "5.13.1",
+  "vocab_size": 49408
+}

text_encoder_2/model.fp16.safetensors

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tokenizer/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_2/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}

unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.39.0",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 128,
+  "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": [
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+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}

unet/diffusion_pytorch_model.fp16.safetensors

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vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.39.0",
+  "_name_or_path": "../sdxl-vae/",
+  "act_fn": "silu",
+  "block_out_channels": [
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+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
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+  "up_block_types": [
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+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}

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