prompt to nft service

projects/prompt-to-nft/container/src/app.py

@@ -5,7 +5,6 @@
 
 import aiohttp
 from eth_abi import decode, encode  # type: ignore
-from infernet_ml.utils.service_models import InfernetInput, JobLocation
 from quart import Quart, request
 from ritual_arweave.file_manager import FileManager
 
@@ -34,6 +33,7 @@ def ensure_env_vars() -> None:
 def create_app() -> Quart:
     app = Quart(__name__)
     ensure_env_vars()
+    temp_file = "image.png"
 
     @app.route("/")
     def index() -> str:
@@ -44,28 +44,30 @@ def index() -> str:
 
     @app.route("/service_output", methods=["POST"])
     async def inference() -> dict[str, Any]:
-        req_data = await request.get_json()
         """
-        InfernetInput has the format:
+        Input data has the format:
             source: (0 on-chain, 1 off-chain)
+            destination: (0 on-chain, 1 off-chain)
             data: dict[str, Any]
         """
-        infernet_input: InfernetInput = InfernetInput(**req_data)
-        temp_file = "image.png"
-
-        match infernet_input:
-            case InfernetInput(source=JobLocation.OFFCHAIN):
-                prompt: str = cast(dict[str, str], infernet_input.data)["prompt"]
-            case InfernetInput(source=JobLocation.ONCHAIN):
-                # On-chain requests are sent as a generalized hex-string which we will
-                # decode to the appropriate format.
-                (prompt, mintTo) = decode(
-                    ["string", "address"], bytes.fromhex(cast(str, infernet_input.data))
-                )
-                log.info("mintTo: %s", mintTo)
-                log.info("prompt: %s", prompt)
-            case _:
-                raise ValueError("Invalid source")
+        req_data: dict[str, Any] = await request.get_json()
+        onchain_source = True if req_data.get("source") == 0 else False
+        onchain_destination = True if req_data.get("destination") == 0 else False
+        data = req_data.get("data")
+
+        if onchain_source:
+            """
+            For on-chain requests, the prompt is sent as a generalized hex-string
+            which we will decode to the appropriate format.
+            """
+            (prompt, mintTo) = decode(
+                ["string", "address"], bytes.fromhex(cast(str, data))
+            )
+            log.info("mintTo: %s", mintTo)
+            log.info("prompt: %s", prompt)
+        else:
+            """For off-chain requests, the prompt is sent as is."""
+            prompt = cast(dict[str, str], data).get("prompt")
 
         # run the inference and download the image to a temp file
         await run_inference(prompt, temp_file)
@@ -74,38 +76,35 @@ async def inference() -> dict[str, Any]:
             Path(temp_file), {"Content-Type": "image/png"}
         )
 
-        match infernet_input:
-            case InfernetInput(destination=JobLocation.OFFCHAIN):
-                """
-                In case of an off-chain request, the result is returned as is.
-                """
-                return {
-                    "prompt": prompt,
-                    "hash": tx.id,
-                    "image_url": f"https://arweave.net/{tx.id}",
-                }
-            case InfernetInput(destination=JobLocation.ONCHAIN):
-                """
-                In case of an on-chain request, the result is returned in the format:
+        if onchain_destination:
+            """
+            For on-chain requests, the result is returned in the format:
                 {
                     "raw_input": str,
                     "processed_input": str,
                     "raw_output": str,
                     "processed_output": str,
                     "proof": str,
                 }
-                refer to: https://docs.ritual.net/infernet/node/advanced/containers for
-                more info.
-                """
-                return {
-                    "raw_input": infernet_input.data,
-                    "processed_input": "",
-                    "raw_output": encode(["string"], [tx.id]).hex(),
-                    "processed_output": "",
-                    "proof": "",
-                }
-            case _:
-                raise ValueError("Invalid destination")
+            refer to: https://docs.ritual.net/infernet/node/advanced/containers for more
+            info.
+            """
+            return {
+                "raw_input": data,
+                "processed_input": "",
+                "raw_output": encode(["string"], [tx.id]).hex(),
+                "processed_output": "",
+                "proof": "",
+            }
+        else:
+            """
+            For off-chain request, the result is returned as is.
+            """
+            return {
+                "prompt": prompt,
+                "hash": tx.id,
+                "image_url": f"https://arweave.net/{tx.id}",
+            }
 
     return app
 

projects/prompt-to-nft/container/src/requirements.txt

@@ -1,4 +1,3 @@
 quart==0.19.4
-infernet-ml==1.0.0
-web3==6.15.0
 tqdm==4.66.4
+web3==6.15.0

projects/prompt-to-nft/stablediffusion/src/requirements.txt

@@ -1,9 +1,6 @@
 diffusers~=0.19
-invisible_watermark~=0.1
-transformers==4.41.2
-accelerate~=0.21
+huggingface-hub==0.23.0
+quart==0.19.4
 safetensors~=0.3
-Quart==0.19.4
-jmespath==1.0.1
-huggingface-hub==0.20.3
-infernet-ml==1.0.0
+torch==2.2.1
+transformers==4.41.2

projects/prompt-to-nft/stablediffusion/src/stable_diffusion_workflow.py

@@ -4,18 +4,18 @@
 import torch
 from diffusers import DiffusionPipeline
 from huggingface_hub import snapshot_download
-from infernet_ml.workflows.inference.base_inference_workflow import (
-    BaseInferenceWorkflow,
-)
 
 
-class StableDiffusionWorkflow(BaseInferenceWorkflow):
+class StableDiffusionWorkflow:
     def __init__(
         self,
         *args: Any,
         **kwargs: Any,
     ):
-        super().__init__(*args, **kwargs)
+        self.args: list[Any] = list(args)
+        self.kwargs: dict[Any, Any] = kwargs
+
+        self.is_setup = False
 
     def do_setup(self) -> Any:
         ignore = [