Nader Arbabian
@@ -0,0 +1,92 @@
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This is the base PyWorker for comfyui. It can be used to create PyWorker that use various models and
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workflows. It provides two endpoints:
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1. `/prompt`: Uses the default comfy workflow defined under `misc/default_workflows`
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2. `/custom_workflow`: Allows the client to send their own comfy workflow with each API request.
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To use the comfyui PyWorker, `$COMFY_MODEL` env variable must be set in the template. Current options are
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`sd3` and `flux`. Each have example clients.
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To add new models, a JSON with name `$COMFY_MODEL.json` must be created under `misc/default_workflows`
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NOTE: default workflows follow this format:
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```json
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||||
{
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"input": {
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"handler": "RawWorkflow",
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||||
"aws_access_key_id": "your-s3-access-key",
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"aws_secret_access_key": "your-s3-secret-access-key",
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||||
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
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"aws_bucket_name": "your-bucket",
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"webhook_url": "your-webhook-url",
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||||
"webhook_extra_params": {},
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"workflow_json": {}
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}
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}
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```
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You can ignore all of these fields except for `workflow_json`.
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Fields written as "{{FOO}}" will be replaced using data from a user request. For example, SD3's workflow has the
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following nodes:
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```json
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||||
"5": {
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"inputs": {
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"width": "{{WIDTH}}",
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"height": "{{HEIGHT}}",
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"batch_size": 1
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},
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||||
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"6": {
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"inputs": {
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"text": "{{PROMPT}}",
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"clip": ["11", 0]
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},
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"class_type": "CLIPTextEncode",
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"_meta": {
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"title": "CLIP Text Encode (Prompt)"
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}
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},
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||||
...
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"17": {
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"inputs": {
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"scheduler": "simple",
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"steps": "{{STEPS}}",
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"denoise": 1,
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"model": ["12", 0]
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},
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||||
"class_type": "BasicScheduler",
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"_meta": {
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||||
"title": "BasicScheduler"
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}
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||||
},
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...
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"25": {
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"inputs": {
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"noise_seed": "{{SEED}}"
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},
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"class_type": "RandomNoise",
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"_meta": {
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"title": "RandomNoise"
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}
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}
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```
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Incoming requests have the following JSON format:
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```json
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{
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prompt: str
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width: int
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height: int
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steps: int
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seed: int
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}
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```
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Each value in those fields with replace the placeholder of the same name in the default workflow.
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See Vast's serverless documentation for more details on how to use comfyui with autoscaler
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@@ -0,0 +1,150 @@
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from urllib.parse import urljoin
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import requests
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from lib.test_utils import print_truncate_res
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"""
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NOTE: this client example uses a custom comfy workflow compatible with SD3 only
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"""
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def call_default_workflow(
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endpoint_group_name: str, api_key: str, server_url: str
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) -> None:
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WORKER_ENDPOINT = "/prompt"
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COST = 100
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route_payload = {
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"endpoint": endpoint_group_name,
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"api_key": api_key,
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"cost": COST,
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}
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response = requests.post(
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urljoin(server_url, "/route/"),
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json=route_payload,
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timeout=4,
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)
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message = response.json()
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url = message["url"]
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auth_data = dict(
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signature=message["signature"],
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cost=message["cost"],
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endpoint=message["endpoint"],
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reqnum=message["reqnum"],
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url=message["url"],
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)
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payload = dict(
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prompt="a fat fluffy cat", width=1024, height=1024, steps=20, seed=123456789
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)
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req_data = dict(payload=payload, auth_data=auth_data)
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url = urljoin(url, WORKER_ENDPOINT)
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print(f"url: {url}")
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response = requests.post(
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url,
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json=req_data,
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)
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print_truncate_res(str(response.json()))
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def call_custom_workflow_for_sd3(
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endpoint_group_name: str, api_key: str, server_url: str
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) -> None:
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WORKER_ENDPOINT = "/custom-workflow"
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COST = 100
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route_payload = {
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"endpoint": endpoint_group_name,
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"api_key": api_key,
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"cost": COST,
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}
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response = requests.post(
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urljoin(server_url, "/route/"),
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json=route_payload,
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timeout=4,
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)
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message = response.json()
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url = message["url"]
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auth_data = dict(
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signature=message["signature"],
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cost=message["cost"],
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endpoint=message["endpoint"],
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reqnum=message["reqnum"],
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url=message["url"],
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)
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workflow = {
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"3": {
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"inputs": {
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"seed": 156680208700286,
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"steps": 20,
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"cfg": 8,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["5", 0],
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},
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"class_type": "KSampler",
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},
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"4": {
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"inputs": {"ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"},
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"class_type": "CheckpointLoaderSimple",
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},
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"5": {
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"inputs": {"width": 512, "height": 512, "batch_size": 1},
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"class_type": "EmptyLatentImage",
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},
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"6": {
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"inputs": {
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"text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle",
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"clip": ["4", 1],
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},
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"class_type": "CLIPTextEncode",
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},
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"7": {
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"inputs": {"text": "text, watermark", "clip": ["4", 1]},
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"class_type": "CLIPTextEncode",
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},
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"8": {
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"inputs": {"samples": ["3", 0], "vae": ["4", 2]},
|
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"class_type": "VAEDecode",
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},
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"9": {
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"inputs": {"filename_prefix": "ComfyUI", "images": ["8", 0]},
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"class_type": "SaveImage",
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},
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}
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# these values should match the values in the custom workflow above,
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# they are used to calculate workload
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custom_fields = dict(
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steps=20,
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width=512,
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height=512,
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)
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req_data = dict(
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payload=dict(custom_fields=custom_fields, workflow=workflow),
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auth_data=auth_data,
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)
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url = urljoin(url, WORKER_ENDPOINT)
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print(f"url: {url}")
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response = requests.post(
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url,
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json=req_data,
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)
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print_truncate_res(str(response.json()))
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if __name__ == "__main__":
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from lib.test_utils import test_args
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args = test_args.parse_args()
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call_default_workflow(
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api_key=args.api_key,
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endpoint_group_name=args.endpoint_group_name,
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server_url=args.server_url,
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)
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call_custom_workflow_for_sd3(
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api_key=args.api_key,
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endpoint_group_name=args.endpoint_group_name,
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server_url=args.server_url,
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)
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@@ -0,0 +1,205 @@
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import sys
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import os
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import json
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import random
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import dataclasses
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import inspect
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from typing import Dict, Any
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from functools import cache
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from math import ceil
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from enum import Enum
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from lib.data_types import ApiPayload, JsonDataException
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with open("workers/comfyui/misc/test_prompts.txt", "r") as f:
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test_prompts = f.readlines()
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class Model(Enum):
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Flux = "flux"
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Sd3 = "sd3"
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def get_request_time(self) -> int:
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match self:
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case Model.Flux:
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return 23
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case Model.Sd3:
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return 6
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@cache
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def get_model() -> Model:
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match os.environ.get("COMFY_MODEL"):
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case "flux":
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return Model.Flux
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case "sd3":
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return Model.Sd3
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case None:
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||||
raise Exception(
|
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"For comfyui pyworker, $COMFY_MODEL must be set in the vast template"
|
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)
|
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case model:
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raise Exception(f"Unsupported comfyui model: {model}")
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@cache
|
||||
def get_request_template() -> str:
|
||||
with open(f"workers/comfyui/misc/default_workflows/{get_model().value}.json") as f:
|
||||
return f.read()
|
||||
|
||||
|
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def count_workload(width: int, height: int, steps: int) -> float:
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||||
"""
|
||||
we want to normalize the workload is a number such that cur_perf(tokens/second) for 1024x1024 image with
|
||||
28 steps is 200 tokens on a 4090.
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||||
|
||||
in order get that we calculate the
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||||
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||||
A = ( absolute workload based on given data )
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B = ( absolute workload for a 1024x1024 image with 28 steps )
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||||
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||||
and adjust the workload to 200 tokens by A/B.
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we then adjust for difference between Flux and SD3 by multiplying this value by expected request time for a
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||||
standard image(23s for Flux, 6s for SD3).
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On a 4090, this would give us a workload that would give a cur_perf(workload / request_time) of around 200
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"""
|
||||
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def _calculate_absolute_tokens(width_: int, height_: int, steps_: int) -> float:
|
||||
"""
|
||||
This is based on how openai counts image generation tokens, see: https://openai.com/api/pricing/
|
||||
|
||||
we count how many 512x512 grids are needed to cover the image.
|
||||
each tile is then counted as 175 tokens.
|
||||
each image generation also has constant of 85 base tokens.
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||||
|
||||
we then adjust the count based on the number of steps. The baseline number of steps is assumed to be 28.
|
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Some testing with flux gave me this data:
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||||
|
||||
steps(X) | request time(Y)
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||||
__________|_________________
|
||||
07(0.25x) | 11s (0.47x)
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||||
14(0.50x) | 15s (0.65x)
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||||
21(0.75x) | 20s (0.86x)
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28(1.00x) | 23s (1.00x)
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35(1.25x) | 28s (1.21x)
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||||
42(1.50x) | 32s (1.39x)
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||||
49(1.75x) | 37s (1.60x)
|
||||
|
||||
this gives a linear regression of Y = 0.61*X + 6.57
|
||||
|
||||
we can use this as an adjustment_factor for token count
|
||||
|
||||
adjustment_factor = (0.61 * steps + 6.57)
|
||||
"""
|
||||
|
||||
width_grids = ceil(width_ / 512)
|
||||
height_grids = ceil(height_ / 512)
|
||||
tokens = 85 + width_grids * height_grids * 175
|
||||
adjustment_factor = 0.61 * steps_ + 6.57
|
||||
return tokens * adjustment_factor
|
||||
|
||||
REQUEST_TIME_FOR_STANDARD_IMAGE = get_model().get_request_time()
|
||||
|
||||
absolute_tokens = _calculate_absolute_tokens(
|
||||
width_=width, height_=height, steps_=steps
|
||||
)
|
||||
absolute_tokens_standard_image = _calculate_absolute_tokens(
|
||||
width_=1024, height_=1024, steps_=28
|
||||
)
|
||||
return REQUEST_TIME_FOR_STANDARD_IMAGE * (
|
||||
(absolute_tokens / absolute_tokens_standard_image) * 200
|
||||
)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class DefaultComfyWorkflowData(ApiPayload):
|
||||
prompt: str
|
||||
width: int
|
||||
height: int
|
||||
steps: int
|
||||
seed: int
|
||||
|
||||
@classmethod
|
||||
def for_test(cls):
|
||||
|
||||
test_prompt = random.choice(test_prompts).rstrip()
|
||||
return cls(
|
||||
prompt=test_prompt,
|
||||
width=1024,
|
||||
height=1024,
|
||||
steps=28,
|
||||
seed=random.randint(0, sys.maxsize),
|
||||
)
|
||||
|
||||
def generate_payload_json(
|
||||
self,
|
||||
) -> Dict[str, Any]:
|
||||
return json.loads(
|
||||
get_request_template()
|
||||
.replace("{{PROMPT}}", self.prompt)
|
||||
# these values should be of int type. Since "{{VAR}}" is wrapped with " in the template
|
||||
# to make the JSON valid, we must replace the double quotes. i.e. "{{WIDTH}}" -> 1024 and not "1024"
|
||||
.replace('"{{WIDTH}}"', str(self.width))
|
||||
.replace('"{{HEIGHT}}"', str(self.height))
|
||||
.replace('"{{STEPS}}"', str(self.steps))
|
||||
.replace('"{{SEED}}"', str(self.seed))
|
||||
)
|
||||
|
||||
def count_workload(self) -> float:
|
||||
return count_workload(width=self.width, height=self.height, steps=self.steps)
|
||||
|
||||
@classmethod
|
||||
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "DefaultComfyWorkflowData":
|
||||
errors = {}
|
||||
for param in inspect.signature(cls).parameters:
|
||||
if param not in json_msg:
|
||||
errors[param] = "missing parameter"
|
||||
if errors:
|
||||
raise JsonDataException(errors)
|
||||
return cls(
|
||||
**{
|
||||
k: v
|
||||
for k, v in json_msg.items()
|
||||
if k in inspect.signature(cls).parameters
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class CustomComfyWorkflowData(ApiPayload):
|
||||
custom_fields: Dict[str, int]
|
||||
workflow: Dict[str, Any]
|
||||
|
||||
@classmethod
|
||||
def for_test(cls):
|
||||
raise NotImplemented("Custom comfy workflow is not used for testing")
|
||||
|
||||
def count_workload(self) -> float:
|
||||
return count_workload(
|
||||
width=int(self.custom_fields.get("width", 1024)),
|
||||
height=int(self.custom_fields.get("height", 1024)),
|
||||
steps=int(self.custom_fields.get("steps", 28)),
|
||||
)
|
||||
|
||||
def generate_payload_json(self) -> Dict[str, Any]:
|
||||
template_json = json.loads(get_request_template())
|
||||
template_json["input"]["workflow_json"] = self.workflow
|
||||
return template_json
|
||||
|
||||
@classmethod
|
||||
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "CustomComfyWorkflowData":
|
||||
errors = {}
|
||||
for param in inspect.signature(cls).parameters:
|
||||
if param not in json_msg:
|
||||
errors[param] = "missing parameter"
|
||||
if errors:
|
||||
raise JsonDataException(errors)
|
||||
return cls(
|
||||
**{
|
||||
k: v
|
||||
for k, v in json_msg.items()
|
||||
if k in inspect.signature(cls).parameters
|
||||
}
|
||||
)
|
||||
@@ -0,0 +1,137 @@
|
||||
{
|
||||
"input": {
|
||||
"handler": "RawWorkflow",
|
||||
"aws_access_key_id": "your-s3-access-key",
|
||||
"aws_secret_access_key": "your-s3-secret-access-key",
|
||||
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
|
||||
"aws_bucket_name": "your-bucket",
|
||||
"webhook_url": "your-webhook-url",
|
||||
"webhook_extra_params": {},
|
||||
"workflow_json": {
|
||||
"5": {
|
||||
"inputs": {
|
||||
"width": "{{WIDTH}}",
|
||||
"height": "{{HEIGHT}}",
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptyLatentImage",
|
||||
"_meta": {
|
||||
"title": "Empty Latent Image"
|
||||
}
|
||||
},
|
||||
"6": {
|
||||
"inputs": {
|
||||
"text": "{{PROMPT}}",
|
||||
"clip": ["11", 0]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Prompt)"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"inputs": {
|
||||
"samples": ["13", 0],
|
||||
"vae": ["10", 0]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"9": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI",
|
||||
"images": ["8", 0]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"10": {
|
||||
"inputs": {
|
||||
"vae_name": "ae.safetensors"
|
||||
},
|
||||
"class_type": "VAELoader",
|
||||
"_meta": {
|
||||
"title": "Load VAE"
|
||||
}
|
||||
},
|
||||
"11": {
|
||||
"inputs": {
|
||||
"clip_name1": "t5xxl_fp16.safetensors",
|
||||
"clip_name2": "clip_l.safetensors",
|
||||
"type": "flux"
|
||||
},
|
||||
"class_type": "DualCLIPLoader",
|
||||
"_meta": {
|
||||
"title": "DualCLIPLoader"
|
||||
}
|
||||
},
|
||||
"12": {
|
||||
"inputs": {
|
||||
"unet_name": "flux1-dev.safetensors",
|
||||
"weight_dtype": "default"
|
||||
},
|
||||
"class_type": "UNETLoader",
|
||||
"_meta": {
|
||||
"title": "Load Diffusion Model"
|
||||
}
|
||||
},
|
||||
"13": {
|
||||
"inputs": {
|
||||
"noise": ["25", 0],
|
||||
"guider": ["22", 0],
|
||||
"sampler": ["16", 0],
|
||||
"sigmas": ["17", 0],
|
||||
"latent_image": ["5", 0]
|
||||
},
|
||||
"class_type": "SamplerCustomAdvanced",
|
||||
"_meta": {
|
||||
"title": "SamplerCustomAdvanced"
|
||||
}
|
||||
},
|
||||
"16": {
|
||||
"inputs": {
|
||||
"sampler_name": "euler"
|
||||
},
|
||||
"class_type": "KSamplerSelect",
|
||||
"_meta": {
|
||||
"title": "KSamplerSelect"
|
||||
}
|
||||
},
|
||||
"17": {
|
||||
"inputs": {
|
||||
"scheduler": "simple",
|
||||
"steps": "{{STEPS}}",
|
||||
"denoise": 1,
|
||||
"model": ["12", 0]
|
||||
},
|
||||
"class_type": "BasicScheduler",
|
||||
"_meta": {
|
||||
"title": "BasicScheduler"
|
||||
}
|
||||
},
|
||||
"22": {
|
||||
"inputs": {
|
||||
"model": ["12", 0],
|
||||
"conditioning": ["6", 0]
|
||||
},
|
||||
"class_type": "BasicGuider",
|
||||
"_meta": {
|
||||
"title": "BasicGuider"
|
||||
}
|
||||
},
|
||||
"25": {
|
||||
"inputs": {
|
||||
"noise_seed": "{{SEED}}"
|
||||
},
|
||||
"class_type": "RandomNoise",
|
||||
"_meta": {
|
||||
"title": "RandomNoise"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,142 @@
|
||||
{
|
||||
"input": {
|
||||
"handler": "RawWorkflow",
|
||||
"aws_access_key_id": "your-s3-access-key",
|
||||
"aws_secret_access_key": "your-s3-secret-access-key",
|
||||
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
|
||||
"aws_bucket_name": "your-bucket",
|
||||
"webhook_url": "your-webhook-url",
|
||||
"webhook_extra_params": {},
|
||||
"workflow_json": {
|
||||
"6": {
|
||||
"inputs": {
|
||||
"text": "{{PROMPT}}",
|
||||
"clip": ["252", 1]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Prompt)"
|
||||
}
|
||||
},
|
||||
"13": {
|
||||
"inputs": {
|
||||
"shift": 3,
|
||||
"model": ["252", 0]
|
||||
},
|
||||
"class_type": "ModelSamplingSD3",
|
||||
"_meta": {
|
||||
"title": "ModelSamplingSD3"
|
||||
}
|
||||
},
|
||||
"67": {
|
||||
"inputs": {
|
||||
"conditioning": ["71", 0]
|
||||
},
|
||||
"class_type": "ConditioningZeroOut",
|
||||
"_meta": {
|
||||
"title": "ConditioningZeroOut"
|
||||
}
|
||||
},
|
||||
"68": {
|
||||
"inputs": {
|
||||
"start": 0.1,
|
||||
"end": 1,
|
||||
"conditioning": ["67", 0]
|
||||
},
|
||||
"class_type": "ConditioningSetTimestepRange",
|
||||
"_meta": {
|
||||
"title": "ConditioningSetTimestepRange"
|
||||
}
|
||||
},
|
||||
"69": {
|
||||
"inputs": {
|
||||
"conditioning_1": ["68", 0],
|
||||
"conditioning_2": ["70", 0]
|
||||
},
|
||||
"class_type": "ConditioningCombine",
|
||||
"_meta": {
|
||||
"title": "Conditioning (Combine)"
|
||||
}
|
||||
},
|
||||
"70": {
|
||||
"inputs": {
|
||||
"start": 0,
|
||||
"end": 0.1,
|
||||
"conditioning": ["71", 0]
|
||||
},
|
||||
"class_type": "ConditioningSetTimestepRange",
|
||||
"_meta": {
|
||||
"title": "ConditioningSetTimestepRange"
|
||||
}
|
||||
},
|
||||
"71": {
|
||||
"inputs": {
|
||||
"text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
|
||||
"clip": ["252", 1]
|
||||
},
|
||||
"class_type": "CLIPTextEncode",
|
||||
"_meta": {
|
||||
"title": "CLIP Text Encode (Negative Prompt)"
|
||||
}
|
||||
},
|
||||
"135": {
|
||||
"inputs": {
|
||||
"width": "{{WIDTH}}",
|
||||
"height": "{{HEIGHT}}",
|
||||
"batch_size": 1
|
||||
},
|
||||
"class_type": "EmptySD3LatentImage",
|
||||
"_meta": {
|
||||
"title": "EmptySD3LatentImage"
|
||||
}
|
||||
},
|
||||
"231": {
|
||||
"inputs": {
|
||||
"samples": ["271", 0],
|
||||
"vae": ["252", 2]
|
||||
},
|
||||
"class_type": "VAEDecode",
|
||||
"_meta": {
|
||||
"title": "VAE Decode"
|
||||
}
|
||||
},
|
||||
"233": {
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI",
|
||||
"images": ["231", 0]
|
||||
},
|
||||
"class_type": "SaveImage",
|
||||
"_meta": {
|
||||
"title": "Save Image"
|
||||
}
|
||||
},
|
||||
"252": {
|
||||
"inputs": {
|
||||
"ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"
|
||||
},
|
||||
"class_type": "CheckpointLoaderSimple",
|
||||
"_meta": {
|
||||
"title": "Load Checkpoint"
|
||||
}
|
||||
},
|
||||
"271": {
|
||||
"inputs": {
|
||||
"seed": "{{SEED}}",
|
||||
"steps": "{{STEPS}}",
|
||||
"cfg": 4.5,
|
||||
"sampler_name": "dpmpp_2m",
|
||||
"scheduler": "sgm_uniform",
|
||||
"denoise": 1,
|
||||
"model": ["13", 0],
|
||||
"positive": ["6", 0],
|
||||
"negative": ["69", 0],
|
||||
"latent_image": ["135", 0]
|
||||
},
|
||||
"class_type": "KSampler",
|
||||
"_meta": {
|
||||
"title": "KSampler"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,34 @@
|
||||
cartoon character of a person with a hoodie , in style of cytus and deemo, ork, gold chains, realistic anime cat, dripping black goo, lineage revolution style, thug life, cute anthropomorphic bunny, balrog, arknights, aliased, very buff, black and red and yellow paint, painting illustration collage style, character composition in vector with white background
|
||||
stardew valley, fine details
|
||||
2D Vector Illustration of a child with soccer ball Art for Sublimation, Design Art, Chrome Art, Painting and Stunning Artwork, Highly Detailed Digital Painting, Airbrush Art, Highly Detailed Digital Artwork, Dramatic Artwork, stained antique yellow copper paint, digital airbrush art, detailed by Mark Brooks, Chicano airbrush art, Swagger! snake Culture
|
||||
realistic futuristic city-downtown with short buildings, sunset
|
||||
seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water
|
||||
inspired by realflow-cinema4d editor features, create image of a transparent luxury cup with ice fruits and mint, connected with white, yellow and pink cream, Slow - High Speed MO Photography, YouTube Video Screenshot, Abstract Clay, Transparent Cup , molecular gastronomy, wheel, 3D fluid,Simulation rendering, still video, 4k polymer clay futras photography, very surreal, Houdini Fluid Simulation, hyperrealistic CGI and FLUIDS & MULTIPHYSICS SIMULATION effect, with Somali Stain Lurex, Metallic Jacquard, Gold Thread, Mulberry Silk, Toub Saree, Warm background, a fantastic image worthy of an award.
|
||||
biker with backpack on his back riding a motorcycle, Style by Ade Santora, Oilpunk, Cover photo, craig mullins style, on the cover of a magazine, Outdoor Magazine, inspired by Alex Petruk APe, image of a male biker, Cover of an award-winning magazine, the man has a backpack, photo for magazine, with a backpack, magazine cover
|
||||
generate a collage-style illustration inspired by the Procreate raster graphic editor, photographic illustration with the theme, 2D vector, art for textile sublimation, containing surrealistic cartoon cat wearing a baseball cap and jeans standing in front of a poster, inspired by Sadao Watanabe, Doraemon, Japanese cartoon style, Eichiro Oda, Iconic high detail character, Director: Nakahara Nantenbō, Kastuhiro Otomo, image detailed, by Miyamoto, Hidetaka Miyazaki, Katsuhiro illustration, 8k, masterpiece, Minimize noise and grain in photo quality without lose quality and increase brightness and lighting,Symmetry and Alignment, Avoid asymmetrical shapes and out-of-focus points. Focus and Sharpness: Make sure the image is focused and sharp and encourages the viewer to see it as a work of art printed on fabric.
|
||||
fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details
|
||||
Iron Man, (Arnold Tsang, Toru Nakayama), Masterpiece, Studio Quality, 6k , toa, toaair, 1boy, glowing, axe, mecha, science_fiction, solo, weapon, jungle , green_background, nature, outdoors, solo, tree, weapon, mask, dynamic lighting, detailed shading, digital texture painting
|
||||
(Pope Francis) wearing leather jacket is a DJ in a nightclub, mixing live on stage, giant mixing table, a masterpiece
|
||||
Pope Francis wearing biker (leather jacket), a masterpiece
|
||||
Luke Skywalker ordering a burger and fries from the Death Star canteen.
|
||||
I want to generate a group avatar for a Feishu group chat. The role of this group is daily software technical communication. Now the subject technology stacks that members of this group discuss daily include: algorithms, data structures, optimization, functional programming, and the programming languages often discussed are: TypeScript, Java, python, etc. I hope this avatar has a simple aesthetic, this avatar is a single person avatar
|
||||
portrait Anime black girl cute-fine-face, pretty face, realistic shaded Perfect face, fine details. Anime. realistic shaded lighting by Ilya Kuvshinov Giuseppe Dangelico Pino and Michael Garmash and Rob Rey, IAMAG premiere, WLOP matte print, cute freckles, masterpiece
|
||||
young Disney socialite wearing a beige miniskirt, dark brown turtleneck sweater, small neckless, cute-fine-face, anime. illustration, realistic shaded perfect face, brown hair, grey eyes, fine details, realistic shaded lighting by ilya kuvshinov giuseppe dangelico pino and michael garmash and rob rey, iamag premiere, wlop matte print, a masterpiece
|
||||
Cute small cat sitting in a movie theater eating chicken wiggs watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
|
||||
Cute small dog sitting in a movie theater eating popcorn watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
|
||||
fox bracelet made of buckskin with fox features, rich details, fine carvings, studio lighting
|
||||
crane buckskin bracelet with crane features, rich details, fine carvings, studio lighting
|
||||
london luxurious interior living-room, light walls
|
||||
Parisian luxurious interior penthouse bedroom, dark walls, wooden panels
|
||||
cute girl, crop-top, blond hair, black glasses, stretching, with background by greg rutkowski makoto shinkai kyoto animation key art feminine mid shot
|
||||
houses in front, houses background, straight houses, digital art, smooth, sharp focus, gravity falls style, doraemon style, shinchan style, anime style
|
||||
Simplified technical drawing, Leonardo da Vinci, Mechanical Dinosaur Skeleton, Minimalistic annotations, Hand-drawn illustrations, Basic design and engineering, Wonder and curiosity
|
||||
High quality 8K painting impressionist style of a Japanese modern city street with a girl on the foreground wearing a traditional wedding dress with a fox mask, staring at the sky, daylight
|
||||
a landscape from the Moon with the Earth setting on the horizon, realistic, detailed
|
||||
Isometric Atlantis city,great architecture with columns, great details, ornaments,seaweed, blue ambiance, 3D cartoon style, soft light, 45° view
|
||||
A hyper realistic avatar of a guy riding on a black honda cbr 650r in leather suit,high detail, high quality,8K,photo realism
|
||||
the street of amedieval fantasy town, at dawn, dark, highly detailed
|
||||
overwhelmingly beautiful eagle framed with vector flowers, long shiny wavy flowing hair, polished, ultra detailed vector floral illustration mixed with hyper realism, muted pastel colors, vector floral details in background, muted colors, hyper detailed ultra intricate overwhelming realism in detailed complex scene with magical fantasy atmosphere, no signature, no watermark
|
||||
a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece | hyperrealism| highly detailed| insanely detailed| intricate| cinematic lighting| depth of field
|
||||
electronik robot and ofice ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
|
||||
exquisitely intricately detailed illustration, of a small world with a lake and a rainbow, inside a closed glass jar.
|
||||
@@ -0,0 +1,135 @@
|
||||
import os
|
||||
import logging
|
||||
import dataclasses
|
||||
import base64
|
||||
from typing import Union, Type
|
||||
|
||||
from aiohttp import web, ClientResponse
|
||||
from anyio import open_file
|
||||
|
||||
from lib.backend import Backend, LogAction
|
||||
from lib.data_types import EndpointHandler
|
||||
from lib.server import start_server
|
||||
from .data_types import DefaultComfyWorkflowData, CustomComfyWorkflowData
|
||||
|
||||
|
||||
MODEL_SERVER_URL = "http://0.0.0.0:38188"
|
||||
|
||||
# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
|
||||
MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: http://127.0.0.1:18188"
|
||||
MODEL_SERVER_ERROR_LOG_MSGS = [
|
||||
"MetadataIncompleteBuffer", # This error is emitted when the downloaded model is corrupted
|
||||
"Value not in list: unet_name", # This error is emitted when the model file is not there at all
|
||||
]
|
||||
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.DEBUG,
|
||||
format="%(asctime)s[%(levelname)-5s] %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
log = logging.getLogger(__file__)
|
||||
|
||||
|
||||
async def generate_client_response(
|
||||
request: web.Request, response: ClientResponse
|
||||
) -> Union[web.Response, web.StreamResponse]:
|
||||
_ = request
|
||||
match response.status:
|
||||
case 200:
|
||||
log.debug("SUCCESS")
|
||||
res = await response.json()
|
||||
if "output" not in res:
|
||||
return web.json_response(
|
||||
data=dict(error="there was an error in the workflow"),
|
||||
status=422,
|
||||
)
|
||||
image_paths = [path["local_path"] for path in res["output"]["images"]]
|
||||
if not image_paths:
|
||||
return web.json_response(
|
||||
data=dict(error="workflow did not produce any images"),
|
||||
status=422,
|
||||
)
|
||||
images = []
|
||||
for image_path in image_paths:
|
||||
async with await open_file(image_path, mode="rb") as f:
|
||||
contents = await f.read()
|
||||
images.append(
|
||||
f"data:image/png;base64,{base64.b64encode(contents).decode('utf-8')}"
|
||||
)
|
||||
return web.json_response(data=dict(images=images))
|
||||
case code:
|
||||
log.debug("SENDING RESPONSE: ERROR: unknown code")
|
||||
return web.Response(status=code)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class DefaultComfyWorkflowHandler(EndpointHandler[DefaultComfyWorkflowData]):
|
||||
|
||||
@property
|
||||
def endpoint(self) -> str:
|
||||
return "/runsync"
|
||||
|
||||
@classmethod
|
||||
def payload_cls(cls) -> Type[DefaultComfyWorkflowData]:
|
||||
return DefaultComfyWorkflowData
|
||||
|
||||
def make_benchmark_payload(self) -> DefaultComfyWorkflowData:
|
||||
return DefaultComfyWorkflowData.for_test()
|
||||
|
||||
async def generate_client_response(
|
||||
self, client_request: web.Request, model_response: ClientResponse
|
||||
) -> Union[web.Response, web.StreamResponse]:
|
||||
return await generate_client_response(client_request, model_response)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class CustomComfyWorkflowHandler(EndpointHandler[CustomComfyWorkflowData]):
|
||||
|
||||
@property
|
||||
def endpoint(self) -> str:
|
||||
return "/runsync"
|
||||
|
||||
@classmethod
|
||||
def payload_cls(cls) -> Type[CustomComfyWorkflowData]:
|
||||
return CustomComfyWorkflowData
|
||||
|
||||
def make_benchmark_payload(self) -> CustomComfyWorkflowData:
|
||||
return CustomComfyWorkflowData.for_test()
|
||||
|
||||
async def generate_client_response(
|
||||
self, client_request: web.Request, model_response: ClientResponse
|
||||
) -> Union[web.Response, web.StreamResponse]:
|
||||
return await generate_client_response(client_request, model_response)
|
||||
|
||||
|
||||
backend = Backend(
|
||||
model_server_url=MODEL_SERVER_URL,
|
||||
model_log_file=os.environ["MODEL_LOG"],
|
||||
allow_parallel_requests=False,
|
||||
benchmark_handler=DefaultComfyWorkflowHandler(
|
||||
benchmark_runs=3, benchmark_words=100
|
||||
),
|
||||
log_actions=[
|
||||
(LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
|
||||
(LogAction.Info, "Downloading:"),
|
||||
*[
|
||||
(LogAction.ModelError, error_msg)
|
||||
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
|
||||
],
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
async def handle_ping(_):
|
||||
return web.Response(body="pong")
|
||||
|
||||
|
||||
routes = [
|
||||
web.post("/prompt", backend.create_handler(DefaultComfyWorkflowHandler())),
|
||||
web.post("/custom-workflow", backend.create_handler(CustomComfyWorkflowHandler())),
|
||||
web.get("/ping", handle_ping),
|
||||
]
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_server(backend, routes)
|
||||
@@ -0,0 +1,15 @@
|
||||
from lib.test_utils import test_load_cmd, test_args
|
||||
from .data_types import DefaultComfyWorkflowData, Model
|
||||
|
||||
WORKER_ENDPOINT = "/prompt"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_args.add_argument(
|
||||
"-m",
|
||||
dest="comfy_model",
|
||||
choices=list(map(lambda x: x.value, Model)),
|
||||
required=True,
|
||||
help="Image generation model name",
|
||||
)
|
||||
test_load_cmd(DefaultComfyWorkflowData, WORKER_ENDPOINT, arg_parser=test_args)
|
||||
Reference in New Issue
Block a user