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3d_render.safetensors

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+size 85450700

Fixhands-unfilteredai.safetensors

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+oid sha256:7fad91117c8205b11b7e7d37b2820ffc68ff526caabee546d54906907e373ed3
+size 3152032

README.md

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+---
+base_model: OEvortex/PixelGen
+license: other
+language:
+- en
+- pt
+- th
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+- UnfilteredAI
+- 3d
+- text-to-image
+- not-for-all-audiences
+---
+
+**Model Name:** NSFW-gen-v2
+
+**ANIME version** [Here](https://huggingface.co/UnfilteredAI/NSFW-GEN-ANIME)
+
+**Type:** Text-to-Image Generator
+
+**Description:** NSFW-gen is a text-to-image generator developed by UnfilteredAI. This model is designed to generate all kinds of images, including explicit and NSFW (Not Safe For Work) images from textual inputs.
+
+**Features:**
+- **Uncensored Output:** The model produces uncensored and potentially explicit images based on textual inputs.
+- **Tensor Type:** Operates with FP16 tensor type for optimized performance and efficiency.
+- **Model Size:** With 3.47 billion parameters, the model offers a vast capacity for learning and generating diverse imagery.
+- **3D Style Rendering:** The model now includes 3D style/image rendering capability to generate more realistic images. (Use 3d, 3d style in your prompt)
+
+**Usage Guidelines:**
+- **Responsible Use:** Exercise discretion and responsibility when generating content with this model.
+- **Age Restriction:** Due to the explicit nature of the generated content, usage is restricted to individuals over the legal age in their jurisdiction.

model.safetensors

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model_index.json

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+{
+  "_class_name": "StableDiffusionXLPipeline",
+  "_diffusers_version": "0.22.0.dev0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "force_zeros_for_empty_prompt": true,
+  "image_encoder": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "EulerAncestralDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}