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Testing Text-to-Image Generation API Endpoints in Stable-Diffusion-WebUI

This test suite validates the text-to-image (txt2img) API endpoint functionality in the Stable Diffusion WebUI. It covers various image generation parameters, sampling methods, and batch processing capabilities through comprehensive HTTP endpoint testing.

Test Coverage Overview

The test suite provides extensive coverage of the txt2img API endpoint functionality.

Key areas tested include:

Basic image generation with default parameters
Negative prompt handling
Complex prompt syntax processing
High-resolution fix functionality
Face restoration features
Different sampler implementations
Batch processing and multiple iteration scenarios

Implementation Analysis

The testing approach utilizes pytest fixtures for API endpoint configuration and request payload management. The implementation follows a modular pattern with parametrized tests for sampler variations and separate test cases for distinct features. Pytest fixtures handle common test setup, reducing code duplication and improving maintainability.

Technical Details

Testing tools and configuration:

pytest framework for test organization
requests library for HTTP API testing
Fixture-based configuration management
Parametrized testing for sampler variations
JSON payload structure for API requests
HTTP status code validation

Best Practices Demonstrated

The test suite exemplifies several testing best practices including isolation of test cases, consistent assertion patterns, and efficient test setup. Notable practices include:

Modular test case organization
Reusable fixture patterns
Comprehensive feature coverage
Clear test naming conventions
Efficient test parameterization

automatic1111/stable-diffusion-webui

test/test_txt2img.py

            

import pytest
import requests


@pytest.fixture()
def url_txt2img(base_url):
    return f"{base_url}/sdapi/v1/txt2img"


@pytest.fixture()
def simple_txt2img_request():
    return {
        "batch_size": 1,
        "cfg_scale": 7,
        "denoising_strength": 0,
        "enable_hr": False,
        "eta": 0,
        "firstphase_height": 0,
        "firstphase_width": 0,
        "height": 64,
        "n_iter": 1,
        "negative_prompt": "",
        "prompt": "example prompt",
        "restore_faces": False,
        "s_churn": 0,
        "s_noise": 1,
        "s_tmax": 0,
        "s_tmin": 0,
        "sampler_index": "Euler a",
        "seed": -1,
        "seed_resize_from_h": -1,
        "seed_resize_from_w": -1,
        "steps": 3,
        "styles": [],
        "subseed": -1,
        "subseed_strength": 0,
        "tiling": False,
        "width": 64,
    }


def test_txt2img_simple_performed(url_txt2img, simple_txt2img_request):
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_with_negative_prompt_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["negative_prompt"] = "example negative prompt"
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_with_complex_prompt_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["prompt"] = "((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]"
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_not_square_image_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["height"] = 128
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_with_hrfix_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["enable_hr"] = True
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_with_tiling_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["tiling"] = True
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_with_restore_faces_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["restore_faces"] = True
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


@pytest.mark.parametrize("sampler", ["PLMS", "DDIM", "UniPC"])
def test_txt2img_with_vanilla_sampler_performed(url_txt2img, simple_txt2img_request, sampler):
    simple_txt2img_request["sampler_index"] = sampler
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_multiple_batches_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["n_iter"] = 2
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200


def test_txt2img_batch_performed(url_txt2img, simple_txt2img_request):
    simple_txt2img_request["batch_size"] = 2
    assert requests.post(url_txt2img, json=simple_txt2img_request).status_code == 200