add pipeline script example

scripts/example.py

@@ -0,0 +1,132 @@
+import gradio as gr
+from diffusers.pipelines import StableDiffusionPipeline, StableDiffusionXLPipeline # pylint: disable=unused-import
+from modules import shared, scripts, processing, sd_models
+
+"""
+Author::
+- Your details
+
+Credits:
+- Link to original implementation and author
+
+Contributions:
+- Submit a PR on SD.Next GitHub repo to be included in /scripts
+- Before submitting a PR, make sure to test your script thoroughly and that it passes code quality checks
+  Lint rules are part of SD.Next CI/CD pipeline
+    > pip install ruff pylint
+    > ruff scripts/example.py
+    > pylint scriptts/example.py
+"""
+
+## Config
+
+# script title
+title = 'Example'
+
+# is script available in txt2img tab
+txt2img = False
+
+# is script available in img2img tab
+img2img = False
+
+# is pipeline ok to run in pure latent mode without implicit conversions
+# recommended so entire ecosystem can be used as-is, but requires that latent is in format that sdnext can understand
+# some pipelines may not support this, in which case set to false and pipeline will implicitly do things like vae encode/decode on its own
+latent = True
+
+# class from which this pipeline is derived, most commonly 'StableDiffusionPipeline' or 'StableDiffusionXLPipeline'
+pipeline_base = 'StableDiffusionPipeline'
+
+# class definition for this pipeline
+# for built-in diffuser pipelines, simply import it from diffusers.pipelines above
+# for example only, its set to same as base pipeline
+# for community pipelines, copy class definition from community source code
+# in which case only class definition code and required imports needs to be copied, not the entire source code
+pipeline_class = StableDiffusionPipeline
+
+# pipeline args values are defined in ui method below, here we need to define their exact names
+# they also have to be in the exact order as they are defined in ui
+# note: variable names should be exactly as defined in pipeline_class.__call__ method
+# if pipeline requires a param and its not provided, it will result in runtime error
+# if you provide param that is not defined by pipeline, sdnext will strip it
+params = ['test1', 'test2', 'test3', 'test4']
+
+
+### Script definition
+
+class Script(scripts.Script):
+    def title(self):
+        return title
+
+    def show(self, is_img2img):
+        if shared.backend == shared.Backend.DIFFUSERS:
+            return img2img if is_img2img else txt2img
+        return False
+
+    # Define UI for pipeline
+    def ui(self, _is_img2img):
+        ui_controls = []
+        with gr.Row():
+            ui_controls.append(gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label="Test1"))
+            ui_controls.append(gr.Slider(minimum=0, maximum=10, step=1, value=5, label="Test2"))
+        with gr.Row():
+            ui_controls.append(gr.Checkbox(label="Test3", value=True))
+        with gr.Row():
+            ui_controls.append(gr.Textbox(label="Test4", value="", placeholder="enter text here"))
+        with gr.Row():
+            gr.HTML('<a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl>Stable Diffusion SDXL pipeline docs</a>"')
+        return ui_controls
+
+    # Run pipeline
+    def run(self, p: processing.StableDiffusionProcessing, *args): # pylint: disable=arguments-differ
+        # prepare pipeline
+        c = shared.sd_model.__class__.__name__ if shared.sd_model is not None else ''
+        if c != pipeline_base:
+            shared.log.warning(f'{title}: pipeline={c} required={pipeline_base}')
+            return None
+        orig_pipepeline = shared.sd_model # backup current pipeline definition
+        shared.sd_model = pipeline_class( # create new pipeline using currently loaded model which is always in `shared.sd_model`
+            # different pipelines may need different init params, so you may need to change this
+            # to see init params, see pipeline_class.__init__ method
+            # if init params are incorrect you will also see a runtime error with unrecognized or missing params
+            # for example:
+            # > TypeError: StableDiffusionPipeline.__init__() missing 2 required positional arguments: 'safety_checker' and 'feature_extractor'
+            vae = shared.sd_model.vae,
+            text_encoder=shared.sd_model.text_encoder,
+            tokenizer=shared.sd_model.tokenizer,
+            unet=shared.sd_model.unet,
+            scheduler=shared.sd_model.scheduler,
+            safety_checker=shared.sd_model.safety_checker,
+            feature_extractor=shared.sd_model.feature_extractor,
+        )
+        if not ((shared.opts.diffusers_model_cpu_offload or shared.cmd_opts.medvram) or (shared.opts.diffusers_seq_cpu_offload or shared.cmd_opts.lowvram)):
+            shared.sd_model.to(shared.device) # move pipeline if needed, but don't touch if its under automatic managment
+        sd_models.set_diffuser_options(shared.sd_model) # set all model options such as fp16, offload, etc.
+
+        # if pipeline also needs a specific type, you can set it here, but not commonly needed
+        # shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
+
+        # prepare params
+        # all pipeline params go into p.task_args and are automatically handled by sdnext from there
+        for i in range(len(args)):
+            p.task_args[params[i]] = args[i]
+
+        # you can also re-use existing params from `p` object if pipeline wants them, but under a different name
+        # for example, if pipeline expects 'image' param, but you want to use 'init_images' instead which is what img2img tab uses
+        # p.task_args['image'] = p.init_images[0]
+
+        if not latent:
+            p.task_args['output_type'] = 'np'
+        shared.log.debug(f'{c}: args={p.task_args}')
+
+        # if you need to run any preprocessing, this is the place to do it
+
+        # run processing
+        processed: processing.Processed = processing.process_images(p)
+
+        # if you need to run any postprocessing, this is the place to do it
+        # you dont need to handle saving, metadata, etc - sdnext will do it for you
+
+        # restore original pipeline
+        shared.sd_model = orig_pipepeline
+        return processed