| Name | runware JSON |
| Version |
0.3.9
JSON |
| download |
| home_page | https://github.com/runware/sdk-python |
| Summary | The Python Runware SDK is used to run image inference with the Runware API, powered by the Runware inference platform. It can be used to generate images with text-to-image and image-to-image. It also allows the use of an existing gallery of models or selecting any model or LoRA from the CivitAI gallery. The API also supports upscaling, background removal, inpainting and outpainting, and a series of other ControlNet models. |
| upload_time | 2024-12-13 12:50:14 |
| maintainer | None |
| docs_url | None |
| author | Runware Inc. |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
runware
stable diffusion
text to image
image to text
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
aiofiles
python-dotenv
websockets
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Python Runware SDK
The Python Runware SDK is used to run image inference with the Runware API, powered by the Runware inference platform. It can be used to generate images with text-to-image and image-to-image. It also allows the use of an existing gallery of models or selecting any model or LoRA from the CivitAI gallery. The API also supports upscaling, background removal, inpainting and outpainting, and a series of other ControlNet models.
## Get API Access
To use the Python Runware SDK, you need to obtain an API key. Follow these steps to get API access:
1. [Create a free account](https://my.runware.ai/) with [Runware](https://runware.ai/).
2. Once you have created an account, you will receive an API key and trial credits.
**Important**: Please keep your API key private and do not share it with anyone. Treat it as a sensitive credential.
## Documentation
For detailed documentation and API reference, please visit the [Runware Documentation](https://docs.runware.ai/) or refer to the [docs](docs) folder in the repository. The documentation provides comprehensive information about the available classes, methods, and parameters, along with code examples to help you get started with the Runware SDK Python.
## Installation
To install the Python Runware SDK, use the following command:
```bash
pip install runware
```
## Usage
Before using the Python Runware SDK, make sure to set your Runware API key in the environment variable `RUNWARE_API_KEY`. You can do this by creating a `.env` file in your project root and adding the following line:
```bash
RUNWARE_API_KEY = "your_api_key_here"
```
### Generating Images
To generate images using the Runware API, you can use the `imageInference` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IImageInference
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
request_image = IImageInference(
positivePrompt="a beautiful sunset over the mountains",
model="civitai:36520@76907",
numberResults=4,
negativePrompt="cloudy, rainy",
height=512,
width=512,
)
images = await runware.imageInference(requestImage=request_image)
for image in images:
print(f"Image URL: {image.imageURL}")
```
### Enhancing Prompts
To enhance prompts using the Runware API, you can use the `promptEnhance` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IPromptEnhance
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
prompt = "A beautiful sunset over the mountains"
prompt_enhancer = IPromptEnhance(
prompt=prompt,
promptVersions=3,
promptMaxLength=64,
)
enhanced_prompts = await runware.promptEnhance(promptEnhancer=prompt_enhancer)
for enhanced_prompt in enhanced_prompts:
print(enhanced_prompt.text)
```
### Removing Image Background
To remove the background from an image using the Runware API, you can use the `imageBackgroundRemoval` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IImageBackgroundRemoval
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
image_path = "image.jpg"
remove_image_background_payload = IImageBackgroundRemoval(image_initiator=image_path)
processed_images = await runware.imageBackgroundRemoval(
removeImageBackgroundPayload=remove_image_background_payload
)
for image in processed_images:
print(image.imageURL)
```
### Image-to-Text Conversion
To convert an image to text using the Runware API, you can use the `imageCaption` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IRequestImageToText
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
image_path = "image.jpg"
request_image_to_text_payload = IImageCaption(image_initiator=image_path)
image_to_text = await runware.imageCaption(
requestImageToText=request_image_to_text_payload
)
print(image_to_text.text)
```
### Upscaling Images
To upscale an image using the Runware API, you can use the `imageUpscale` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IImageUpscale
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
image_path = "image.jpg"
upscale_factor = 4
upscale_gan_payload = IImageUpscale(
inputImage=image_path, upscaleFactor=upscale_factor
)
upscaled_images = await runware.imageUpscale(upscaleGanPayload=upscale_gan_payload)
for image in upscaled_images:
print(image.imageSrc)
```
### Photo Maker
Use the `photoMaker` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IPhotoMaker
import uuid
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
request_image = IPhotoMaker(
model="civitai:101055@128080",
positivePrompt="img of a beautiful lady in a forest",
steps=35,
numberResults=1,
height=512,
width=512,
style="No style",
strength=40,
outputFormat="WEBP",
includeCost=True,
taskUUID=str(uuid.uuid4()),
inputImages=[
"https://im.runware.ai/image/ws/0.5/ii/74723926-22f6-417c-befb-f2058fc88c13.webp",
"https://im.runware.ai/image/ws/0.5/ii/64acee31-100d-4aa1-a47e-6f8b432e7188.webp",
"https://im.runware.ai/image/ws/0.5/ii/1b39b0e0-6bf7-4c9a-8134-c0251b5ede01.webp",
"https://im.runware.ai/image/ws/0.5/ii/f4b4cec3-66d9-4c02-97c5-506b8813182a.webp"
],
)
photos = await runware.photoMaker(requestPhotoMaker=request_image)
for photo in photos:
print(f"Image URL: {photo.imageURL}")
```
### Generating Images with refiner
To generate images using the Runware API with refiner support, you can use the `imageInference` method of the `Runware` class. Here's an example:
```python
from runware import Runware, IImageInference, IRefiner
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
refiner = IRefiner(
model="civitai:101055@128080",
startStep=a,
startStepPercentage=None,
)
request_image = IImageInference(
positivePrompt="a beautiful sunset over the mountains",
model="civitai:36520@76907",
numberResults=4,
negativePrompt="cloudy, rainy",
height=512,
width=512,
refiner=refiner
)
images = await runware.imageInference(requestImage=request_image)
for image in images:
print(f"Image URL: {image.imageURL}")
```
### Model Upload
To upload model using the Runware API, you can use the `uploadModel` method of the `Runware` class. Here are examples:
```python
from runware import Runware, IImageInference, IRefiner, IUploadModelCheckPoint
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
payload = IUploadModelCheckPoint(
air='qatests:68487@08629',
name='yWO8IaKwez',
heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',
downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'
'/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',
uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',
version='1.0',
tags=['tag1', 'tag2', 'tag2'],
architecture='flux1d',
type='base',
defaultWeight=0.8,
format='safetensors',
positiveTriggerWords='my trigger word',
shortDescription='a model description',
private=False,
defaultScheduler='Default',
comment='some comments if you want to add for internal use',
)
uploaded = await runware.modelUpload(payload)
print(f"Response : {uploaded}")
```
```python
from runware import Runware, IImageInference, IRefiner, IUploadModelLora
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
payload = IUploadModelLora(
air='qatests:68487@08629',
name='yWO8IaKwez',
heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',
downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'
'/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',
uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',
version='1.0',
tags=['tag1', 'tag2', 'tag2'],
architecture='flux1d',
type='base',
defaultWeight=0.8,
format='safetensors',
positiveTriggerWords='my trigger word',
shortDescription='a model description',
private=False,
comment='some comments if you want to add for internal use',
)
uploaded = await runware.modelUpload(payload)
print(f"Response : {uploaded}")
```
```python
from runware import Runware, IImageInference, IRefiner, IUploadModelControlNet
async def main() -> None:
runware = Runware(api_key=RUNWARE_API_KEY)
await runware.connect()
payload = IUploadModelControlNet(
air='qatests:68487@08629',
name='yWO8IaKwez',
heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',
downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'
'/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',
uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',
version='1.0',
tags=['tag1', 'tag2', 'tag2'],
architecture='flux1d',
type='base',
format='safetensors',
positiveTriggerWords='my trigger word',
shortDescription='a model description',
private=False,
comment='some comments if you want to add for internal use',
)
uploaded = await runware.modelUpload(payload)
print(f"Response : {uploaded}")
```
For more detailed usage and additional examples, please refer to the examples directory.
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"description": "# Python Runware SDK\n\nThe Python Runware SDK is used to run image inference with the Runware API, powered by the Runware inference platform. It can be used to generate images with text-to-image and image-to-image. It also allows the use of an existing gallery of models or selecting any model or LoRA from the CivitAI gallery. The API also supports upscaling, background removal, inpainting and outpainting, and a series of other ControlNet models.\n\n## Get API Access\n\nTo use the Python Runware SDK, you need to obtain an API key. Follow these steps to get API access:\n\n1. [Create a free account](https://my.runware.ai/) with [Runware](https://runware.ai/).\n2. Once you have created an account, you will receive an API key and trial credits.\n\n**Important**: Please keep your API key private and do not share it with anyone. Treat it as a sensitive credential.\n\n## Documentation\n\nFor detailed documentation and API reference, please visit the [Runware Documentation](https://docs.runware.ai/) or refer to the [docs](docs) folder in the repository. The documentation provides comprehensive information about the available classes, methods, and parameters, along with code examples to help you get started with the Runware SDK Python.\n\n## Installation\n\nTo install the Python Runware SDK, use the following command:\n\n```bash\npip install runware\n```\n\n## Usage\n\nBefore using the Python Runware SDK, make sure to set your Runware API key in the environment variable `RUNWARE_API_KEY`. You can do this by creating a `.env` file in your project root and adding the following line:\n\n```bash\nRUNWARE_API_KEY = \"your_api_key_here\"\n```\n\n### Generating Images\n\nTo generate images using the Runware API, you can use the `imageInference` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IImageInference\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n request_image = IImageInference(\n positivePrompt=\"a beautiful sunset over the mountains\",\n model=\"civitai:36520@76907\", \n numberResults=4, \n negativePrompt=\"cloudy, rainy\",\n height=512, \n width=512, \n )\n\n images = await runware.imageInference(requestImage=request_image)\n for image in images:\n print(f\"Image URL: {image.imageURL}\")\n```\n\n### Enhancing Prompts\n\nTo enhance prompts using the Runware API, you can use the `promptEnhance` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IPromptEnhance\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n prompt = \"A beautiful sunset over the mountains\"\n prompt_enhancer = IPromptEnhance(\n prompt=prompt,\n promptVersions=3,\n promptMaxLength=64,\n )\n\n enhanced_prompts = await runware.promptEnhance(promptEnhancer=prompt_enhancer)\n for enhanced_prompt in enhanced_prompts:\n print(enhanced_prompt.text)\n```\n\n### Removing Image Background\n\nTo remove the background from an image using the Runware API, you can use the `imageBackgroundRemoval` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IImageBackgroundRemoval\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n image_path = \"image.jpg\"\n remove_image_background_payload = IImageBackgroundRemoval(image_initiator=image_path)\n\n processed_images = await runware.imageBackgroundRemoval(\n removeImageBackgroundPayload=remove_image_background_payload\n )\n for image in processed_images:\n print(image.imageURL)\n```\n\n### Image-to-Text Conversion\n\nTo convert an image to text using the Runware API, you can use the `imageCaption` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IRequestImageToText\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n image_path = \"image.jpg\"\n request_image_to_text_payload = IImageCaption(image_initiator=image_path)\n\n image_to_text = await runware.imageCaption(\n requestImageToText=request_image_to_text_payload\n )\n print(image_to_text.text)\n```\n\n### Upscaling Images\n\nTo upscale an image using the Runware API, you can use the `imageUpscale` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IImageUpscale\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n image_path = \"image.jpg\"\n upscale_factor = 4\n\n upscale_gan_payload = IImageUpscale(\n inputImage=image_path, upscaleFactor=upscale_factor\n )\n upscaled_images = await runware.imageUpscale(upscaleGanPayload=upscale_gan_payload)\n for image in upscaled_images:\n print(image.imageSrc)\n```\n\n### Photo Maker\n\nUse the `photoMaker` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IPhotoMaker\nimport uuid\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n request_image = IPhotoMaker(\n model=\"civitai:101055@128080\",\n positivePrompt=\"img of a beautiful lady in a forest\",\n steps=35,\n numberResults=1,\n height=512,\n width=512,\n style=\"No style\",\n strength=40,\n outputFormat=\"WEBP\",\n includeCost=True,\n taskUUID=str(uuid.uuid4()),\n inputImages=[\n \"https://im.runware.ai/image/ws/0.5/ii/74723926-22f6-417c-befb-f2058fc88c13.webp\",\n \"https://im.runware.ai/image/ws/0.5/ii/64acee31-100d-4aa1-a47e-6f8b432e7188.webp\",\n \"https://im.runware.ai/image/ws/0.5/ii/1b39b0e0-6bf7-4c9a-8134-c0251b5ede01.webp\",\n \"https://im.runware.ai/image/ws/0.5/ii/f4b4cec3-66d9-4c02-97c5-506b8813182a.webp\"\n ],\n )\n \n \n photos = await runware.photoMaker(requestPhotoMaker=request_image)\n for photo in photos:\n print(f\"Image URL: {photo.imageURL}\")\n```\n\n### Generating Images with refiner\n\nTo generate images using the Runware API with refiner support, you can use the `imageInference` method of the `Runware` class. Here's an example:\n\n```python\nfrom runware import Runware, IImageInference, IRefiner\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n \n refiner = IRefiner(\n model=\"civitai:101055@128080\",\n startStep=a,\n startStepPercentage=None,\n )\n\n request_image = IImageInference(\n positivePrompt=\"a beautiful sunset over the mountains\",\n model=\"civitai:36520@76907\", \n numberResults=4, \n negativePrompt=\"cloudy, rainy\",\n height=512, \n width=512, \n refiner=refiner\n )\n\n images = await runware.imageInference(requestImage=request_image)\n for image in images:\n print(f\"Image URL: {image.imageURL}\")\n```\n\n### Model Upload\n\nTo upload model using the Runware API, you can use the `uploadModel` method of the `Runware` class. Here are examples:\n\n```python\nfrom runware import Runware, IImageInference, IRefiner, IUploadModelCheckPoint\n\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n payload = IUploadModelCheckPoint(\n air='qatests:68487@08629',\n name='yWO8IaKwez',\n heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',\n downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'\n '/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',\n uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',\n version='1.0',\n tags=['tag1', 'tag2', 'tag2'],\n architecture='flux1d',\n type='base',\n defaultWeight=0.8,\n format='safetensors',\n positiveTriggerWords='my trigger word',\n shortDescription='a model description',\n private=False,\n defaultScheduler='Default',\n comment='some comments if you want to add for internal use',\n )\n\n uploaded = await runware.modelUpload(payload)\n print(f\"Response : {uploaded}\")\n```\n\n```python\nfrom runware import Runware, IImageInference, IRefiner, IUploadModelLora\n\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n payload = IUploadModelLora(\n air='qatests:68487@08629',\n name='yWO8IaKwez',\n heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',\n downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'\n '/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',\n uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',\n version='1.0',\n tags=['tag1', 'tag2', 'tag2'],\n architecture='flux1d',\n type='base',\n defaultWeight=0.8,\n format='safetensors',\n positiveTriggerWords='my trigger word',\n shortDescription='a model description',\n private=False,\n comment='some comments if you want to add for internal use',\n )\n\n uploaded = await runware.modelUpload(payload)\n print(f\"Response : {uploaded}\")\n```\n\n```python\nfrom runware import Runware, IImageInference, IRefiner, IUploadModelControlNet\n\n\nasync def main() -> None:\n runware = Runware(api_key=RUNWARE_API_KEY)\n await runware.connect()\n\n payload = IUploadModelControlNet(\n air='qatests:68487@08629',\n name='yWO8IaKwez',\n heroImageUrl='https://raw.githubusercontent.com/adilentiq/test-images/refs/heads/main/image.jpg',\n downloadUrl='https://repo-controlnets-r2.runware.ai/controlnet-zoe-depth-sdxl-1.0.safetensors'\n '/controlnet-zoe-depth-sdxl-1.0.safetensors.part-001-1',\n uniqueIdentifier='aq2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1q2w3e4r5t6y7u8i9o0p1234',\n version='1.0',\n tags=['tag1', 'tag2', 'tag2'],\n architecture='flux1d',\n type='base',\n format='safetensors',\n positiveTriggerWords='my trigger word',\n shortDescription='a model description',\n private=False,\n comment='some comments if you want to add for internal use',\n )\n\n\nuploaded = await runware.modelUpload(payload)\nprint(f\"Response : {uploaded}\")\n```\n\nFor more detailed usage and additional examples, please refer to the examples directory.\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "The Python Runware SDK is used to run image inference with the Runware API, powered by the Runware inference platform. It can be used to generate images with text-to-image and image-to-image. It also allows the use of an existing gallery of models or selecting any model or LoRA from the CivitAI gallery. The API also supports upscaling, background removal, inpainting and outpainting, and a series of other ControlNet models.",
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