text-to-image

Author: Wauplin

packages/inference/src/snippets/python.ts

@@ -143,8 +143,13 @@ const snippetGenerator = (templateName: string, inputPreparationFn?: InputPrepar
 				params["methodName"] = HFH_INFERENCE_CLIENT_METHODS[model.pipeline_tag];
 			}
 
-			/// Handle import section separately
+			/// Generate snippet
 			let snippet = template(params).trim();
+			if (!snippet) {
+				return;
+			}
+
+			/// Add import section separately
 			if (tool === "huggingface_hub") {
 				const importSection = snippetImportInferenceClient({ ...params });
 				snippet = `${importSection}\n\n${snippet}`;

packages/inference/src/snippets/templates/python/fal_client/textToImage.jinja

@@ -4,7 +4,7 @@ import fal_client
 result = fal_client.subscribe(
     "{{ providerModelId }}",
     arguments={
-        "prompt": {{ inputs }},
+        "prompt": {{ inputs.asObj.inputs }},
     },
 )
 print(result)

packages/inference/src/snippets/templates/python/huggingface_hub/textToImage.jinja

@@ -1,5 +1,5 @@
 # output is a PIL.Image object
 image = client.text_to_image(
-    {{ inputs }},
+    {{ inputs.asObj.inputs }},
     model="{{ model.id }}",
 ) 

packages/inference/src/snippets/templates/python/requests/textToImage.jinja

@@ -4,7 +4,7 @@ def query(payload):
     return response.content
 
 image_bytes = query({
-    "inputs": {{ inputs }},
+    "inputs": {{ inputs.asObj.inputs }},
 })
 
 # You can access the image with PIL.Image for example

packages/tasks-gen/scripts/generate-snippets-fixtures.ts

@@ -134,17 +134,17 @@ const TEST_CASES: {
 		providers: ["hf-inference"],
 		languages: ["py"],
 	},
-	// {
-	// 	testName: "text-to-image",
-	// 	model: {
-	// 		id: "black-forest-labs/FLUX.1-schnell",
-	// 		pipeline_tag: "text-to-image",
-	// 		tags: [],
-	// 		inference: "",
-	// 	},
-	// 	providers: ["hf-inference", "fal-ai"],
-	// 	languages: ["sh", "js", "py"],
-	// },
+	{
+		testName: "text-to-image",
+		model: {
+			id: "black-forest-labs/FLUX.1-schnell",
+			pipeline_tag: "text-to-image",
+			tags: [],
+			inference: "",
+		},
+		providers: ["hf-inference", "fal-ai"],
+		languages: ["sh", "js", "py"],
+	},
 	// {
 	// 	testName: "text-to-video",
 	// 	model: {

packages/tasks-gen/snippets-fixtures/text-to-image/0.curl.hf-inference.sh

@@ -0,0 +1,5 @@
+curl https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell \
+	-X POST \
+	-d '{"inputs": "Astronaut riding a horse"}' \
+	-H 'Content-Type: application/json' \
+	-H 'Authorization: Bearer api_token'

packages/tasks-gen/snippets-fixtures/text-to-image/0.huggingface.js.fal-ai.js

@@ -0,0 +1,11 @@
+import { InferenceClient } from "@huggingface/inference";
+
+const client = new InferenceClient("api_token");
+
+const image = await client.textToImage({
+	model: "black-forest-labs/FLUX.1-schnell",
+	inputs: "Astronaut riding a horse",
+	parameters: { num_inference_steps: 5 },
+	provider: "fal-ai",
+});
+/// Use the generated image (it's a Blob)

packages/tasks-gen/snippets-fixtures/text-to-image/0.huggingface.js.hf-inference.js

@@ -0,0 +1,11 @@
+import { InferenceClient } from "@huggingface/inference";
+
+const client = new InferenceClient("api_token");
+
+const image = await client.textToImage({
+	model: "black-forest-labs/FLUX.1-schnell",
+	inputs: "Astronaut riding a horse",
+	parameters: { num_inference_steps: 5 },
+	provider: "hf-inference",
+});
+/// Use the generated image (it's a Blob)

packages/tasks-gen/snippets-fixtures/text-to-image/0.huggingface_hub.fal-ai.py

@@ -0,0 +1,12 @@
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(
+    provider="fal-ai",
+    api_key="api_token",
+)
+
+# output is a PIL.Image object
+image = client.text_to_image(
+    "Astronaut riding a horse",
+    model="black-forest-labs/FLUX.1-schnell",
+)

packages/tasks-gen/snippets-fixtures/text-to-image/0.huggingface_hub.hf-inference.py

@@ -0,0 +1,12 @@
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(
+    provider="hf-inference",
+    api_key="api_token",
+)
+
+# output is a PIL.Image object
+image = client.text_to_image(
+    "Astronaut riding a horse",
+    model="black-forest-labs/FLUX.1-schnell",
+)

packages/tasks-gen/snippets-fixtures/text-to-image/1.fal_client.fal-ai.py

@@ -0,0 +1,9 @@
+import fal_client
+
+result = fal_client.subscribe(
+    "<fal-ai alias for black-forest-labs/FLUX.1-schnell>",
+    arguments={
+        "prompt": "Astronaut riding a horse",
+    },
+)
+print(result)

packages/tasks-gen/snippets-fixtures/text-to-image/1.fetch.hf-inference.js

@@ -0,0 +1,18 @@
+async function query(data) {
+	const response = await fetch(
+		"https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell",
+		{
+			headers: {
+				Authorization: "Bearer api_token",
+				"Content-Type": "application/json",
+			},
+			method: "POST",
+			body: JSON.stringify(data),
+		}
+	);
+	const result = await response.blob();
+	return result;
+}
+query({"inputs": "Astronaut riding a horse"}).then((response) => {
+	// Use image
+});

packages/tasks-gen/snippets-fixtures/text-to-image/1.requests.hf-inference.py

@@ -0,0 +1,17 @@
+import requests
+
+API_URL = "https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell"
+headers = {"Authorization": "Bearer api_token"}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+image_bytes = query({
+    "inputs": "Astronaut riding a horse",
+})
+
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))