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README.md

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+---
+tags:
+- stable-diffusion-xl
+- stable-diffusion-xl-diffusers
+- text-to-image
+- diffusers
+- lora
+- template:sd-lora
+widget:
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of a city with a repeating pattern of blue, green, and yellow colors. The
+    blue and green colors are scattered across the map, creating a mosaic-like effect.
+    The yellow and blue colors are larger and more prominent, with some areas being
+    darker and others being lighter. The map appears to be overlaid on top of a larger
+    map, with a white background. The overall color scheme of the map is predominantly
+    blue, with hints of green and yellow.
+  output:
+    url: image-0.png
+- text: in the style of <s0><s1>, procedural generation; The image is a 3D map of
+    a small island in the middle of a body of water. The island is covered in green
+    and brown vegetation, with patches of blue and green. The water is a deep blue-green
+    color, and there are several small islands scattered throughout the island. In
+    the center of the island, there is a large white area with a red dot in the center,
+    which appears to be a small town or village. The red dot is likely a location
+    marker or a marker for a specific location. The map is set against a dark blue
+    background, making the colors of the vegetation stand out.
+  output:
+    url: image-1.png
+- text: in the style of <s0><s1>, procedural generation; The image is an abstract
+    digital art piece that appears to be made up of different colors and shapes. The
+    background is divided into different shades of blue, green, orange, and yellow,
+    creating a chaotic and dynamic composition. In the center of the image, there
+    is a large, dark blue blob that is the focal point. The blob is outlined in a
+    lighter shade of blue and has a pink dot in the middle. Surrounding the blob are
+    various shapes and lines that create a sense of depth and dimension. The overall
+    effect is one of movement and energy.
+  output:
+    url: image-2.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of the world, with different colors representing different regions. The colors
+    are predominantly green, yellow, and blue, with some areas being darker and others
+    being lighter. The map appears to be a topographic representation of the continents
+    and oceans, with the majority of the colors being green and the majority being
+    yellow.\n\nThe map is divided into different sections, with each section having
+    a different color - yellow, orange, blue, and green. The yellow section is in
+    the center of the map, with a darker shade of green on the left side and a lighter
+    shade of blue on the right side. The blue section is on the top right corner,
+    and the green section is at the bottom left corner. There are also several smaller
+    blue areas scattered throughout the map. The overall color scheme of the image
+    is warm and earthy, with shades of yellow, green, and orange.
+  output:
+    url: image-3.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of a small island in the middle of a blue ocean. The island is made up of
+    different shades of green and pink, with a darker shade of green on the top and
+    lighter shades of pink on the bottom. In the center of the island, there is a
+    large white circle with a small white dot in the center. The circle is surrounded
+    by smaller pink and green areas. On the right side of the image, there are two
+    smaller pink areas, one on the left side and one in the bottom right corner. The
+    background is a solid blue color.
+  output:
+    url: image-4.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of the island of Santorini, Greece. The map is color-coded, with different
+    shades of green, beige, and brown representing different areas of the country.
+    The green areas are darker in color, while the beige areas are lighter in color.
+    The brown areas are scattered throughout the map, with some areas being darker
+    and others being lighter.\n\nThe map is set against a blue background, with the
+    ocean visible in the top left corner. There are also several smaller islands scattered
+    throughout, including a small island in the center of the map. The islands are
+    outlined in white, and there are a few smaller islands on the top right corner
+    of the image. Overall, the map appears to be a detailed representation of the
+    Mediterranean Sea, with various geographical features such as mountains, rivers,
+    and islands visible.
+  output:
+    url: image-5.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of the Mediterranean Sea, which is a deep blue color. The map is color-coded,
+    with different shades of green, orange, and yellow representing different areas
+    of the sea. The green areas are densely packed together, while the orange areas
+    are scattered throughout the map. The yellow areas are larger and more densely
+    packed, with some areas appearing darker and others appearing lighter.\n\nThe
+    map also shows the extent of the ocean floor, with the majority of the land covered
+    in green and orange areas. There are also some areas that appear to be smaller
+    and more flat, with a few smaller areas scattered throughout. The ocean floor
+    is also colored in shades of blue and green, with hints of orange and yellow.
+    The overall color scheme of the map is predominantly blue, green, and orange.
+  output:
+    url: image-6.png
+- text: in the style of <s0><s1>, simplex noise terrain generation
+  output:
+    url: image-7.png
+- text: 'in the style of <s0><s1>, simplex noise terrain generation; '
+  output:
+    url: image-8.png
+- text: in the style of <s0><s1>, simplex noise terrain generation
+  output:
+    url: image-9.png
+- text: in the style of <s0><s1>, simplex noise terrain generation
+  output:
+    url: image-10.png
+- text: in the style of <s0><s1>, simplex noise terrain generation
+  output:
+    url: image-11.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, landform
+  output:
+    url: image-12.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, landform
+  output:
+    url: image-13.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, abstract pattern
+  output:
+    url: image-14.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, landform
+  output:
+    url: image-15.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, abstract pattern
+  output:
+    url: image-16.png
+- text: in the style of <s0><s1>, pixelated grid landform, cellular automata; The
+    image is a pixelated map of the world. The map is made up of small squares of
+    different colors, including blue, green, orange, and gray. The squares are arranged
+    in a grid-like pattern, with some overlapping each other. In the center of the
+    map, there is a large orange flower with a yellow center. The flower appears to
+    be floating in the air, with its petals spread out. The background of the image
+    is dark blue, making the colors of the flower stand out.
+  output:
+    url: image-17.png
+- text: in the style of <s0><s1>, pixelated grid landform, cellular automata; The
+    image is a pixelated map of a city or town. The map is made up of small squares
+    of different colors, including green, blue, and gray. The squares are arranged
+    in a grid-like pattern, with some overlapping each other. In the center of the
+    map, there is a blue square with a white outline. The blue square is slightly
+    larger than the green square, and it appears to be floating in the air. The gray
+    squares are scattered throughout the map. The overall color scheme of the image
+    is green, gray, and blue.
+  output:
+    url: image-18.png
+- text: in the style of <s0><s1>, pixelated grid landform, cellular automata
+  output:
+    url: image-19.png
+- text: in the style of <s0><s1>, simplex noise terrain generation; The image is a
+    satellite view of a large body of water, possibly a lake or a river. The water
+    is a deep blue color and appears to be calm and still. The surface of the water
+    is covered in patches of green and yellow vegetation, with some areas appearing
+    to be densely packed together. The vegetation is mostly green, with patches of
+    yellow and brown scattered throughout. The image is taken from a top-down perspective,
+    looking down on the water and the surrounding area. The overall color scheme of
+    the image is predominantly green, blue, and yellow.
+  output:
+    url: image-20.png
+- text: in the style of <s0><s1>, procedural generation
+  output:
+    url: image-21.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    map of the world in a camouflage pattern. The map is divided into different shades
+    of blue, green, yellow, and brown, creating a camouflage-like effect. The colors
+    are arranged in a way that creates a sense of depth and dimension, with the blue
+    representing the ocean and the green representing the land. The brown represents
+    the land, while the yellow represents the sea. The overall color scheme of the
+    map is predominantly blue and green, with some areas being darker and others being
+    lighter.
+  output:
+    url: image-22.png
+- text: in the style of <s0><s1>, perlin noise terrain generation; The image is a
+    seamless pattern with a camouflage-like design. The background is a combination
+    of brown and yellow colors, with the majority of the colors being a darker shade
+    of brown. The pattern is made up of small, irregularly shaped spots in various
+    shades of yellow and purple. The spots are arranged in a random, overlapping manner,
+    creating a sense of depth and dimension. The overall effect is a textured, abstract
+    design with a mix of different shades and textures.
+  output:
+    url: image-23.png
+- text: 'in the style of <s0><s1>, simplex noise terrain generation; '
+  output:
+    url: image-24.png
+- text: in the style of <s0><s1>, simplex noise terrain generation
+  output:
+    url: image-25.png
+- text: in the style of <s0><s1>, voronoi noise terrain generation, abstract pattern
+  output:
+    url: image-26.png
+- text: in the style of <s0><s1>, simplex noise terrain generation; The image is a
+    square with a green background and a white border. In the center of the square,
+    there is a map of the world in a pixelated style. The map is divided into different
+    shades of green, yellow, and blue, with some areas in gray and others in blue.
+    The colors are arranged in a way that creates a mosaic-like effect, with the green
+    being the dominant color and the yellow being the smallest. The blue areas are
+    scattered throughout the map, creating a sense of depth and dimension. The overall
+    color scheme of the image is predominantly green and yellow, with hints of blue
+    and gray.'
+  output:
+    url: image-27.png
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+instance_prompt: in the style of <s0><s1>
+license: openrail++
+---
+
+# SDXL LoRA DreamBooth - chechiamah/proceduralgenerationforms
+
+<Gallery />
+
+## Model description
+
+### These are chechiamah/proceduralgenerationforms LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
+
+## Download model
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`proceduralgenerationforms.safetensors` here 💾](/chechiamah/proceduralgenerationforms/blob/main/proceduralgenerationforms.safetensors)**.
+    - Place it on your `models/Lora` folder.
+    - On AUTOMATIC1111, load the LoRA by adding `<lora:proceduralgenerationforms:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
+- *Embeddings*: download **[`proceduralgenerationforms_emb.safetensors` here 💾](/chechiamah/proceduralgenerationforms/blob/main/proceduralgenerationforms_emb.safetensors)**.
+    - Place it on it on your `embeddings` folder
+    - Use it by adding `proceduralgenerationforms_emb` to your prompt. For example, `in the style of proceduralgenerationforms_emb`
+    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
+    
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+        
+pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('chechiamah/proceduralgenerationforms', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='chechiamah/proceduralgenerationforms', filename='proceduralgenerationforms_emb.safetensors' repo_type="model")
+state_dict = load_file(embedding_path)
+pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
+        
+image = pipeline('in the style of <s0><s1>').images[0]
+```
+
+For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
+
+## Trigger words
+
+To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
+
+to trigger concept `TOK` → use `<s0><s1>` in your prompt 
+
+
+
+## Details
+All [Files & versions](/chechiamah/proceduralgenerationforms/tree/main).
+
+The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
+
+LoRA for the text encoder was enabled. False.
+
+Pivotal tuning was enabled: True.
+
+Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
+

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.0001
+adam_weight_decay_text_encoder: null
+allow_tf32: false
+cache_dir: null
+cache_latents: true
+caption_column: prompt
+center_crop: false
+checkpointing_steps: 100000
+checkpoints_total_limit: null
+class_data_dir: null
+class_prompt: null
+crops_coords_top_left_h: 0
+crops_coords_top_left_w: 0
+dataloader_num_workers: 0
+dataset_config_name: null
+dataset_name: ./91818384-d2cf-4cdc-9099-69711a7c4756
+enable_xformers_memory_efficient_attention: false
+gradient_accumulation_steps: 1
+gradient_checkpointing: true
+hub_model_id: null
+hub_token: null
+image_column: image
+instance_data_dir: null
+instance_prompt: in the style of <s0><s1>
+learning_rate: 1.0
+local_rank: -1
+logging_dir: logs
+lr_num_cycles: 1
+lr_power: 1.0
+lr_scheduler: constant
+lr_warmup_steps: 0
+max_grad_norm: 1.0
+max_train_steps: 1500
+mixed_precision: bf16
+noise_offset: 0
+num_class_images: 100
+num_new_tokens_per_abstraction: 2
+num_train_epochs: 108
+num_validation_images: 4
+optimizer: prodigy
+output_dir: proceduralgenerationforms
+pretrained_model_name_or_path: stabilityai/stable-diffusion-xl-base-1.0
+pretrained_vae_model_name_or_path: madebyollin/sdxl-vae-fp16-fix
+prior_generation_precision: null
+prior_loss_weight: 1.0
+prodigy_beta3: null
+prodigy_decouple: true
+prodigy_safeguard_warmup: true
+prodigy_use_bias_correction: true
+push_to_hub: false
+rank: 32
+repeats: 1
+report_to: tensorboard
+resolution: 1024
+resume_from_checkpoint: null
+revision: null
+sample_batch_size: 4
+scale_lr: false
+seed: 42
+snr_gamma: null
+text_encoder_lr: 1.0
+token_abstraction: TOK
+train_batch_size: 2
+train_text_encoder: false
+train_text_encoder_frac: 1.0
+train_text_encoder_ti: true
+train_text_encoder_ti_frac: 0.5
+use_8bit_adam: false
+validation_epochs: 50
+validation_prompt: null
+variant: null
+with_prior_preservation: false

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