<div align="center">
<h2>
Custom Diffusion: Creating Video from Frame Using Diffusion
</h2>
<div>
<a href="https://pepy.tech/project/custom_diffusion"><img src="https://pepy.tech/badge/custom_diffusion" alt="downloads"></a>
<a href="https://badge.fury.io/py/custom_diffusion"><img src="https://badge.fury.io/py/custom_diffusion.svg" alt="pypi version"></a>
<a href="https://huggingface.co/spaces/ArtGAN/Stable-Diffusion-ControlNet-WebUI"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
</div>
</div>
## Installation
```bash
pip install diffusersplus
```
## Usage
### Stable Diffusion Text2Image Generate:
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="stable-txt2img",
stable_model_id="dreamlike-art/dreamlike-anime-1.0",
scheduler_name="DDIM"
)
output = model(
prompt="A photo of a anime character",
negative_prompt="bad",
num_images_per_prompt=1,
num_inference_steps=30,
guidance_scale=7.0,
guidance_rescale=0.0,
generator_seed=0,
height=512,
width=512,
)
```
### Stable Diffusion Image2Image Generate:
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="stable-img2img", stable_model_id="dreamlike-art/dreamlike-anime-1.0", scheduler_name="DDIM"
)
output = model(
image_path="../data/image.png",
prompt="A photo of a cat.",
negative_prompt="bad",
num_images_per_prompt=1,
num_inference_steps=50,
guidance_scale=7.0,
strength=0.5,
generator_seed=0,
resize_type="center_crop_and_resize",
crop_size=512,
height=512,
width=512,
)
### Stable Diffusion Upscale:
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="stable-upscale", stable_model_id="stabilityai/stable-diffusion-x4-upscaler", scheduler_name="DDIM"
)
output = model(
image_path="../data/image.png",
prompt="A photo of a anime character.",
negative_prompt="bad",
resize_type="center_crop_and_resize",
noise_level=20,
num_images_per_prompt=1,
num_inference_steps=20,
guidance_scale=7.0,
generator_seed=0,
)
```
### Controlnet:
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="controlnet",
stable_model_id="dreamlike-art/dreamlike-anime-1.0",
controlnet_model_id="lllyasviel/sd-controlnet-canny",
scheduler_name="DDIM",
)
output = model(
image_path="../data/image.png",
prompt="A photo of cat.",
negative_prompt="bad",
height=512,
width=512,
preprocess_type="Canny",
resize_type="center_crop_and_resize",
guess_mode=False,
num_images_per_prompt=1,
num_inference_steps=50,
guidance_scale=7.0,
controlnet_conditioning_scale=0.2,
generator_seed=0,
)
```
### Controlnet Inpaint
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="controlnet-inpaint",
stable_model_id="dreamlike-art/dreamlike-anime-1.0",
controlnet_model_id="lllyasviel/sd-controlnet-canny",
scheduler_name="DDIM",
)
output = model(
image_path="../data/image.png",
mask_path="../data/mask_image.png",
prompt="A photo of a cat.",
negative_prompt="bad",
height=512,
width=512,
preprocess_type="Canny",
resize_type="center_crop_and_resize",
strength=0.5,
guess_mode=False,
num_images_per_prompt=1,
num_inference_steps=50,
guidance_scale=7.0,
controlnet_conditioning_scale=1.0,
generator_seed=0,
)
```
### Controlnet Image2Image
```python
from diffusersplus.automodel import diffusion_pipeline
model = diffusion_pipeline(
task_id="controlnet-img2img",
stable_model_id="dreamlike-art/dreamlike-anime-1.0",
controlnet_model_id="lllyasviel/sd-controlnet-canny",
scheduler_name="DDIM",
)
output = model(
image_path="../data/image.png",
prompt="A photo of a cat.",
negative_prompt="bad",
height=512,
width=512,
preprocess_type="Canny",
resize_type="center_crop_and_resize",
guess_mode=False,
num_images_per_prompt=1,
num_inference_steps=20,
guidance_scale=7.0,
controlnet_conditioning_scale=1.0,
strength=0.5,
generator_seed=0,
)
```
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"description": "<div align=\"center\">\r\n<h2>\r\n Custom Diffusion: Creating Video from Frame Using Diffusion\r\n</h2>\r\n<div>\r\n <a href=\"https://pepy.tech/project/custom_diffusion\"><img src=\"https://pepy.tech/badge/custom_diffusion\" alt=\"downloads\"></a>\r\n <a href=\"https://badge.fury.io/py/custom_diffusion\"><img src=\"https://badge.fury.io/py/custom_diffusion.svg\" alt=\"pypi version\"></a>\r\n <a href=\"https://huggingface.co/spaces/ArtGAN/Stable-Diffusion-ControlNet-WebUI\"><img src=\"https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg\" alt=\"HuggingFace Spaces\"></a>\r\n</div>\r\n</div>\r\n\r\n\r\n## Installation\r\n```bash\r\npip install diffusersplus\r\n```\r\n\r\n## Usage\r\n\r\n### Stable Diffusion Text2Image Generate:\r\n```python\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"stable-txt2img\", \r\n stable_model_id=\"dreamlike-art/dreamlike-anime-1.0\", \r\n scheduler_name=\"DDIM\"\r\n)\r\n\r\noutput = model(\r\n prompt=\"A photo of a anime character\",\r\n negative_prompt=\"bad\",\r\n num_images_per_prompt=1,\r\n num_inference_steps=30,\r\n guidance_scale=7.0,\r\n guidance_rescale=0.0,\r\n generator_seed=0,\r\n height=512,\r\n width=512,\r\n)\r\n```\r\n### Stable Diffusion Image2Image Generate:\r\n\r\n```python\t\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"stable-img2img\", stable_model_id=\"dreamlike-art/dreamlike-anime-1.0\", scheduler_name=\"DDIM\"\r\n)\r\n\r\noutput = model(\r\n image_path=\"../data/image.png\",\r\n prompt=\"A photo of a cat.\",\r\n negative_prompt=\"bad\",\r\n num_images_per_prompt=1,\r\n num_inference_steps=50,\r\n guidance_scale=7.0,\r\n strength=0.5,\r\n generator_seed=0,\r\n resize_type=\"center_crop_and_resize\",\r\n crop_size=512,\r\n height=512,\r\n width=512,\r\n)\r\n\r\n### Stable Diffusion Upscale:\r\n```python\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"stable-upscale\", stable_model_id=\"stabilityai/stable-diffusion-x4-upscaler\", scheduler_name=\"DDIM\"\r\n)\r\n\r\noutput = model(\r\n image_path=\"../data/image.png\",\r\n prompt=\"A photo of a anime character.\",\r\n negative_prompt=\"bad\",\r\n resize_type=\"center_crop_and_resize\",\r\n noise_level=20,\r\n num_images_per_prompt=1,\r\n num_inference_steps=20,\r\n guidance_scale=7.0,\r\n generator_seed=0,\r\n)\r\n```\r\n### Controlnet:\r\n```python\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"controlnet\",\r\n stable_model_id=\"dreamlike-art/dreamlike-anime-1.0\",\r\n controlnet_model_id=\"lllyasviel/sd-controlnet-canny\",\r\n scheduler_name=\"DDIM\",\r\n)\r\noutput = model(\r\n image_path=\"../data/image.png\",\r\n prompt=\"A photo of cat.\",\r\n negative_prompt=\"bad\",\r\n height=512,\r\n width=512,\r\n preprocess_type=\"Canny\",\r\n resize_type=\"center_crop_and_resize\",\r\n guess_mode=False,\r\n num_images_per_prompt=1,\r\n num_inference_steps=50,\r\n guidance_scale=7.0,\r\n controlnet_conditioning_scale=0.2,\r\n generator_seed=0,\r\n)\r\n```\r\n\r\n### Controlnet Inpaint\r\n```python\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"controlnet-inpaint\",\r\n stable_model_id=\"dreamlike-art/dreamlike-anime-1.0\",\r\n controlnet_model_id=\"lllyasviel/sd-controlnet-canny\",\r\n scheduler_name=\"DDIM\",\r\n)\r\noutput = model(\r\n image_path=\"../data/image.png\",\r\n mask_path=\"../data/mask_image.png\",\r\n prompt=\"A photo of a cat.\",\r\n negative_prompt=\"bad\",\r\n height=512,\r\n width=512,\r\n preprocess_type=\"Canny\",\r\n resize_type=\"center_crop_and_resize\",\r\n strength=0.5,\r\n guess_mode=False,\r\n num_images_per_prompt=1,\r\n num_inference_steps=50,\r\n guidance_scale=7.0,\r\n controlnet_conditioning_scale=1.0,\r\n generator_seed=0,\r\n)\r\n```\r\n\r\n### Controlnet Image2Image\r\n```python\r\nfrom diffusersplus.automodel import diffusion_pipeline\r\n\r\nmodel = diffusion_pipeline(\r\n task_id=\"controlnet-img2img\",\r\n stable_model_id=\"dreamlike-art/dreamlike-anime-1.0\",\r\n controlnet_model_id=\"lllyasviel/sd-controlnet-canny\",\r\n scheduler_name=\"DDIM\",\r\n)\r\noutput = model(\r\n image_path=\"../data/image.png\",\r\n prompt=\"A photo of a cat.\",\r\n negative_prompt=\"bad\",\r\n height=512,\r\n width=512,\r\n preprocess_type=\"Canny\",\r\n resize_type=\"center_crop_and_resize\",\r\n guess_mode=False,\r\n num_images_per_prompt=1,\r\n num_inference_steps=20,\r\n guidance_scale=7.0,\r\n controlnet_conditioning_scale=1.0,\r\n strength=0.5,\r\n generator_seed=0,\r\n)\r\n```\r\n",
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