read chain params per stage
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parent
07662b22df
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441a47a885
GPG Key ID:
3EED7B957D362AF1
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@ -388,18 +388,16 @@ def chain(server: ServerContext, pool: DevicePoolExecutor):
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validate(data, schema)
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# get defaults from the regular parameters
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device, params, size = pipeline_from_request(server)
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output = make_output_name(server, "chain", params, size)
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job_name = output[0]
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replace_wildcards(params, get_wildcard_data())
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device, _params, _size = pipeline_from_request(server)
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pipeline = ChainPipeline()
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for stage_data in data.get("stages", []):
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stage_class = CHAIN_STAGES[stage_data.get("type")]
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kwargs: Dict[str, Any] = stage_data.get("params", {})
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logger.info("request stage: %s, %s", stage_class.__name__, kwargs)
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_device, params, size = pipeline_from_request(server, data=kwargs)
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replace_wildcards(params, get_wildcard_data())
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if "control" in kwargs:
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logger.warning("TODO: resolve controlnet model")
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kwargs.pop("control")
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@ -447,6 +445,9 @@ def chain(server: ServerContext, pool: DevicePoolExecutor):
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logger.info("running chain pipeline with %s stages", len(pipeline.stages))
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output = make_output_name(server, "chain", params, size, count=len(pipeline.stages))
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job_name = output[0]
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# build and run chain pipeline
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pool.submit(
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job_name,
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@ -34,11 +34,17 @@ from .utils import get_model_path
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logger = getLogger(__name__)
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def pipeline_from_json(
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server: ServerContext,
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data: Dict[str, Any],
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default_pipeline: str = "txt2img",
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def pipeline_from_request(
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server: ServerContext,
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data: Dict[str, str] = None,
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default_pipeline: str = "txt2img",
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) -> Tuple[DeviceParams, ImageParams, Size]:
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user = request.remote_addr
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if data is None:
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data = request.args
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# platform stuff
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device = None
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device_name = data.get("platform")
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@ -51,6 +57,12 @@ def pipeline_from_json(
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model = get_not_empty(data, "model", get_config_value("model"))
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model_path = get_model_path(server, model)
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control = None
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control_name = data.get("control")
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for network in get_network_models():
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if network.name == control_name:
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control = network
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# pipeline stuff
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pipeline = get_from_list(
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data, "pipeline", get_available_pipelines(), default_pipeline
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@ -149,165 +161,6 @@ def pipeline_from_json(
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# this one can safely use np.random because it produces a single value
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seed = np.random.randint(np.iinfo(np.int32).max)
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logger.debug(
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"parsed parameters for %s steps of %s using %s in %s on %s, %sx%s, %s, %s - %s",
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steps,
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scheduler,
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model_path,
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pipeline,
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device or "any device",
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width,
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height,
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cfg,
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seed,
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prompt,
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)
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params = ImageParams(
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model_path,
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pipeline,
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scheduler,
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prompt,
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cfg,
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steps,
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seed,
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eta=eta,
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negative_prompt=negative_prompt,
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batch=batch,
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# TODO: control=control,
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loopback=loopback,
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tiled_vae=tiled_vae,
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tiles=tiles,
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overlap=overlap,
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stride=stride,
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)
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size = Size(width, height)
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return (device, params, size)
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def pipeline_from_request(
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server: ServerContext,
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default_pipeline: str = "txt2img",
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) -> Tuple[DeviceParams, ImageParams, Size]:
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user = request.remote_addr
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# platform stuff
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device = None
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device_name = request.args.get("platform")
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if device_name is not None and device_name != "any":
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for platform in get_available_platforms():
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if platform.device == device_name:
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device = platform
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# diffusion model
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model = get_not_empty(request.args, "model", get_config_value("model"))
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model_path = get_model_path(server, model)
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control = None
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control_name = request.args.get("control")
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for network in get_network_models():
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if network.name == control_name:
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control = network
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# pipeline stuff
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pipeline = get_from_list(
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request.args, "pipeline", get_available_pipelines(), default_pipeline
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)
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scheduler = get_from_list(request.args, "scheduler", get_pipeline_schedulers())
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if scheduler is None:
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scheduler = get_config_value("scheduler")
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# prompt does not come from config
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prompt = request.args.get("prompt", "")
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negative_prompt = request.args.get("negativePrompt", None)
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if negative_prompt is not None and negative_prompt.strip() == "":
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negative_prompt = None
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# image params
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batch = get_and_clamp_int(
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request.args,
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"batch",
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get_config_value("batch"),
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get_config_value("batch", "max"),
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get_config_value("batch", "min"),
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)
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cfg = get_and_clamp_float(
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request.args,
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"cfg",
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get_config_value("cfg"),
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get_config_value("cfg", "max"),
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get_config_value("cfg", "min"),
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)
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eta = get_and_clamp_float(
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request.args,
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"eta",
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get_config_value("eta"),
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get_config_value("eta", "max"),
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get_config_value("eta", "min"),
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)
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loopback = get_and_clamp_int(
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request.args,
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"loopback",
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get_config_value("loopback"),
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get_config_value("loopback", "max"),
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get_config_value("loopback", "min"),
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)
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steps = get_and_clamp_int(
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request.args,
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"steps",
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get_config_value("steps"),
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get_config_value("steps", "max"),
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get_config_value("steps", "min"),
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)
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height = get_and_clamp_int(
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request.args,
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"height",
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get_config_value("height"),
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get_config_value("height", "max"),
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get_config_value("height", "min"),
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)
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width = get_and_clamp_int(
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request.args,
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"width",
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get_config_value("width"),
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get_config_value("width", "max"),
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get_config_value("width", "min"),
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)
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tiled_vae = get_boolean(request.args, "tiledVAE", get_config_value("tiledVAE"))
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tiles = get_and_clamp_int(
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request.args,
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"tiles",
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get_config_value("tiles"),
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get_config_value("tiles", "max"),
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get_config_value("tiles", "min"),
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)
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overlap = get_and_clamp_float(
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request.args,
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"overlap",
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get_config_value("overlap"),
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get_config_value("overlap", "max"),
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get_config_value("overlap", "min"),
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)
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stride = get_and_clamp_int(
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request.args,
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"stride",
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get_config_value("stride"),
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get_config_value("stride", "max"),
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get_config_value("stride", "min"),
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)
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if stride > tiles:
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logger.info("limiting stride to tile size, %s > %s", stride, tiles)
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stride = tiles
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seed = int(request.args.get("seed", -1))
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if seed == -1:
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# this one can safely use np.random because it produces a single value
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seed = np.random.randint(np.iinfo(np.int32).max)
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logger.info(
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"request from %s: %s steps of %s using %s in %s on %s, %sx%s, %s, %s - %s",
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user,
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