added stable-diffusion pipeline

frontend/core/pipelines/stable-diffusion-2/new-python-1k08gnfkag96/Dockerfile

@@ -0,0 +1,8 @@
+FROM python:3.9
+
+WORKDIR /app
+
+RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+RUN pip install diffusers transformers accelerate scipy safetensors
+
+COPY computations.py .

frontend/core/pipelines/stable-diffusion-2/new-python-1k08gnfkag96/chatHistory.json

@@ -0,0 +1,25 @@
+{
+  "index": 0,
+  "history": [
+    {
+      "timestamp": 1731600898832,
+      "prompt": "Code Template",
+      "response": "def compute(in1, in2):\r\n    \"\"\"A textual description of the compute function.\r\n\r\n    Inputs:\r\n        in1 (all): Textual description of in1\r\n        in2 (all): Textual description of in2\r\n\r\n    Outputs:\r\n        out1 (all): Textual description of out1\r\n        out2 (all): Textual description of out2\r\n\r\n    Requirements:\r\n    \"\"\"\r\n    # some code\r\n    out1 = 2 * in1\r\n    out2 = \"This is the in2 string:\" + in2\r\n\r\n    return {\"out1\": out1, \"out2\": out2}\r\n\r\n\r\ndef test():\r\n    \"\"\"Test the compute function.\"\"\"\r\n\r\n    print(\"Running test\")\r\n"
+    },
+    {
+      "timestamp": 1731601223108,
+      "prompt": "Manual Edit of computations.py",
+      "response": "def compute(prompt, inference_steps):\n    \"\"\"\n    prompt: text description of image\n    inference_steps: difussion steps\n\n    output: generated image path\n    \"\"\"\n    from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\n\n    model_id = \"stabilityai/stable-diffusion-2\"\n    \n    # Use the Euler scheduler here instead\n    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n    pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\n    pipe = pipe.to(\"cuda\")\n    \n    image = pipe(prompt, num_inference_steps=inference_steps).images[0]\n        \n    image.save(\"result.png\")\n\n    return {\"generated_image_path\": \"result.png\"}\n\n\ndef test():\n    \"\"\"Test the compute function.\"\"\"\n\n    print(\"Running test\")\n"
+    },
+    {
+      "timestamp": 1731601351634,
+      "prompt": "Manual Edit of computations.py",
+      "response": "def compute(prompt, inference_steps):\n    \"\"\"\n    prompt: text description of image\n    inference_steps: difussion steps\n\n    output: generated image path\n\n    use GPU to run this pipeline, we are using float16 dtype\n    \"\"\"\n    from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\n\n    model_id = \"stabilityai/stable-diffusion-2\"\n    \n    # Use the Euler scheduler here instead\n    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n    pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\n    pipe = pipe.to(\"cuda\")\n    \n    image = pipe(prompt, num_inference_steps=inference_steps).images[0]\n        \n    image.save(\"result.png\")\n\n    return {\"generated_image_path\": \"result.png\"}\n\n\ndef test():\n    \"\"\"Test the compute function.\"\"\"\n\n    print(\"Running test\")\n"
+    },
+    {
+      "timestamp": 1731603317095,
+      "prompt": "Manual Edit of computations.py",
+      "response": "def compute(prompt, inference_steps):\n    \"\"\"\n    prompt: text description of image\n    inference_steps: difussion steps\n\n    output: generated image path\n\n    use GPU to run this pipeline, we are using float16 dtype\n    \"\"\"\n    from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\n    import torch \n    model_id = \"stabilityai/stable-diffusion-2\"\n    \n    # Use the Euler scheduler here instead\n    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n    pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\n    pipe = pipe.to(\"cuda\")\n    \n    image = pipe(prompt, num_inference_steps=inference_steps).images[0]\n        \n    image.save(\"result.png\")\n\n    return {\"generated_image_path\": \"result.png\"}\n\n\ndef test():\n    \"\"\"Test the compute function.\"\"\"\n\n    print(\"Running test\")\n"
+    }
+  ]
+}

frontend/core/pipelines/stable-diffusion-2/new-python-1k08gnfkag96/computations.py

@@ -0,0 +1,31 @@
+def compute(prompt, inference_steps):
+    """
+    Pipeline to generate the images based on textual description. takes text and inference step as an inputs and return the genreated image.
+
+    prompt: text description of image
+    inference_steps: difussion steps
+
+    output: generated image path
+
+    use GPU to run this pipeline, we are using float16 dtype
+    """
+    from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
+    import torch 
+    model_id = "stabilityai/stable-diffusion-2"
+
+    # Use the Euler scheduler here instead
+    scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
+    pipe = pipe.to("cuda")
+
+    image = pipe(prompt, num_inference_steps=inference_steps).images[0]
+
+    image.save("result.png")
+
+    return {"generated_image_path": "result.png"}
+
+
+def test():
+    """Test the compute function."""
+
+    print("Running test")

frontend/core/pipelines/stable-diffusion-2/new-python-1k08gnfkag96/specs.json

@@ -0,0 +1,92 @@
+{
+  "information": {
+    "id": "stable-diffusion-2",
+    "name": "Stable Diffusion 2",
+    "description": "Pipeline to generate the images based on textual description. takes text and inference step as an inputs and return the genreated image.\nprompt: text description of image\ninference_steps: difussion steps\n\noutput: generated image path\n\nuse GPU to run this pipeline, we are using float16 dtype",
+    "system_versions": [
+      "0.1"
+    ],
+    "block_version": "block version number",
+    "block_source": "core/blocks/stable-diffusion-2",
+    "block_type": "compute"
+  },
+  "inputs": {
+    "prompt": {
+      "type": "Any",
+      "connections": [
+        {
+          "block": "parameter-hhz20qr4vytg",
+          "variable": "parameter"
+        }
+      ]
+    },
+    "inference_steps": {
+      "type": "Any",
+      "connections": [
+        {
+          "block": "parameter-cuha2229jdsa",
+          "variable": "parameter"
+        }
+      ]
+    }
+  },
+  "outputs": {
+    "generated_image_path": {
+      "type": "Any",
+      "connections": [
+        {
+          "block": "view-images-gwly233ys03y",
+          "variable": "image_paths_view"
+        }
+      ]
+    }
+  },
+  "action": {
+    "container": {
+      "image": "stable-diffusion-2",
+      "version": "stable-diffusion-2-1k08gnfkag96",
+      "command_line": [
+        "python",
+        "-u",
+        "entrypoint.py"
+      ]
+    },
+    "resources": {
+      "cpu": {
+        "request": "",
+        "limit": ""
+      },
+      "memory": {
+        "request": "",
+        "limit": ""
+      },
+      "gpu": {
+        "count": 1
+      }
+    }
+  },
+  "views": {
+    "node": {
+      "active": "True or False",
+      "title_bar": {
+        "background_color": "#6b2be0"
+      },
+      "preview": {},
+      "html": "",
+      "pos_x": "786",
+      "pos_y": "188",
+      "pos_z": "999",
+      "behavior": "modal",
+      "order": {
+        "input": [
+          "prompt",
+          "inference_steps"
+        ],
+        "output": [
+          "generated_image_path"
+        ]
+      }
+    }
+  },
+  "events": {}
+}