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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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