small fixes to the fluxsamplerparameters node
This commit is contained in:
cubiq
+35
-42
@@ -247,14 +247,13 @@ class FluxSamplerParams:
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"conditioning": ("CONDITIONING", ),
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"latent_image": ("LATENT", ),
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"noise": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "?" }),
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"seed": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "?" }),
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"sampler": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "euler" }),
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"scheduler": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "simple" }),
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"steps": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "20" }),
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"guidance": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "3.5" }),
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"max_shift": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "" }),
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"base_shift": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "" }),
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"split_sigmas": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "" }),
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"denoise": ("STRING", { "multiline": False, "dynamicPrompts": False, "default": "1.0" }),
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}}
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@@ -263,17 +262,17 @@ class FluxSamplerParams:
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FUNCTION = "execute"
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CATEGORY = "essentials/sampling"
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def execute(self, model, conditioning, latent_image, noise, sampler, scheduler, steps, guidance, max_shift, base_shift, split_sigmas, denoise):
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def execute(self, model, conditioning, latent_image, seed, sampler, scheduler, steps, guidance, max_shift, base_shift, denoise):
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import random
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import time
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from comfy_extras.nodes_custom_sampler import Noise_RandomNoise, BasicScheduler, BasicGuider, SamplerCustomAdvanced, SplitSigmasDenoise
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from comfy_extras.nodes_custom_sampler import Noise_RandomNoise, BasicScheduler, BasicGuider, SamplerCustomAdvanced
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from comfy_extras.nodes_latent import LatentBatch
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from comfy_extras.nodes_model_advanced import ModelSamplingFlux, ModelSamplingAuraFlow
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from node_helpers import conditioning_set_values
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is_schnell = model.model.model_type == comfy.model_base.ModelType.FLOW
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noise = noise.replace("\n", ",").split(",")
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noise = seed.replace("\n", ",").split(",")
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noise = [random.randint(0, 999999) if "?" in n else int(n) for n in noise]
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if not noise:
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noise = [random.randint(0, 999999)]
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@@ -325,10 +324,7 @@ class FluxSamplerParams:
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max_shift = parse_string_to_list(max_shift)
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base_shift = parse_string_to_list(base_shift)
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split_sigmas = "1.0" if split_sigmas == "" else split_sigmas
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split_sigmas = parse_string_to_list(split_sigmas)
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cond_text = None
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if isinstance(conditioning, dict) and "encoded" in conditioning:
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cond_text = conditioning["text"]
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@@ -344,12 +340,11 @@ class FluxSamplerParams:
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samplercustomadvanced = SamplerCustomAdvanced()
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latentbatch = LatentBatch()
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modelsamplingflux = ModelSamplingFlux() if not is_schnell else ModelSamplingAuraFlow()
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splitsigmadenoise = SplitSigmasDenoise()
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width = latent_image["samples"].shape[3]*8
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height = latent_image["samples"].shape[2]*8
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# count total number of samples
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total_samples = len(cond_encoded) * len(noise) * len(max_shift) * len(base_shift) * len(guidance) * len(sampler) * len(scheduler) * len(steps) * len(denoise) * len(split_sigmas)
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total_samples = len(cond_encoded) * len(noise) * len(max_shift) * len(base_shift) * len(guidance) * len(sampler) * len(scheduler) * len(steps) * len(denoise)
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current_sample = 0
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if total_samples > 1:
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pbar = ProgressBar(total_samples)
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@@ -374,33 +369,30 @@ class FluxSamplerParams:
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for st in steps:
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for d in denoise:
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sigmas = basicschedueler.get_sigmas(work_model, sc, st, d)[0]
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for ss in split_sigmas:
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current_sample += 1
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logging.info(f"Sampling {current_sample}/{total_samples} with seed {n}, sampler {s}, scheduler {sc}, steps {st}, guidance {g}, max_shift {ms}, base_shift {bs}, denoise {d}, split_sigmas {ss}")
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sigmas = splitsigmadenoise.get_sigmas(sigmas, ss)[1]
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start_time = time.time()
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latent = samplercustomadvanced.sample(randnoise, guider, samplerobj, sigmas, latent_image)[1]
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elapsed_time = time.time() - start_time
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out_params.append({"time": elapsed_time,
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"seed": n,
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"width": width,
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"height": height,
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"sampler": s,
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"scheduler": sc,
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"steps": st,
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"guidance": g,
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"max_shift": ms,
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"base_shift": bs,
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"denoise": d,
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"split_sigmas": ss,
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"prompt": ct})
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current_sample += 1
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logging.info(f"Sampling {current_sample}/{total_samples} with seed {n}, sampler {s}, scheduler {sc}, steps {st}, guidance {g}, max_shift {ms}, base_shift {bs}, denoise {d}")
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start_time = time.time()
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latent = samplercustomadvanced.sample(randnoise, guider, samplerobj, sigmas, latent_image)[1]
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elapsed_time = time.time() - start_time
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out_params.append({"time": elapsed_time,
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"seed": n,
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"width": width,
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"height": height,
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"sampler": s,
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"scheduler": sc,
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"steps": st,
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"guidance": g,
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"max_shift": ms,
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"base_shift": bs,
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"denoise": d,
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"prompt": ct})
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if out_latent is None:
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out_latent = latent
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else:
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out_latent = latentbatch.batch(out_latent, latent)[0]
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if total_samples > 1:
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pbar.update(1)
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if out_latent is None:
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out_latent = latent
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else:
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out_latent = latentbatch.batch(out_latent, latent)[0]
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if total_samples > 1:
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pbar.update(1)
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return (out_latent, out_params)
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@@ -410,8 +402,8 @@ class PlotParameters:
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return {"required": {
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"images": ("IMAGE", ),
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"params": ("SAMPLER_PARAMS", ),
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"order_by": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift", "split_sigmas"], ),
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"cols_value": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift", "split_sigmas"], ),
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"order_by": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift"], ),
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"cols_value": (["none", "time", "seed", "steps", "denoise", "sampler", "scheduler", "guidance", "max_shift", "base_shift"], ),
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"cols_num": ("INT", {"default": -1, "min": -1, "max": 1024 }),
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"add_prompt": (["false", "true", "excerpt"], ),
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}}
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@@ -429,13 +421,14 @@ class PlotParameters:
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raise ValueError("Number of images and number of parameters do not match.")
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if order_by != "none":
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if cols_value != "none" and cols_num < 1:
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cols_num = len(set(p[cols_value] for p in params))
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sorted_params = sorted(params, key=lambda x: x[order_by])
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indices = [params.index(item) for item in sorted_params]
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params = sorted_params
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images = images[torch.tensor(indices)]
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if cols_value != "none" and cols_num < 1:
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cols_num = len(set(p[cols_value] for p in params))
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width = images.shape[2]
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out_image = []
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@@ -447,7 +440,7 @@ class PlotParameters:
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for (image, param) in zip(images, params):
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image = image.permute(2, 0, 1)
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text = f"time: {param['time']:.2f}s, seed: {param['seed']}, steps: {param['steps']}, size: {param['width']}×{param['height']}\ndenoise: {param['denoise']}, sampler: {param['sampler']}, sched: {param['scheduler']}\nguidance: {param['guidance']}, max/base shift: {param['max_shift']}/{param['base_shift']}, sigmas: {param['split_sigmas']}"
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text = f"time: {param['time']:.2f}s, seed: {param['seed']}, steps: {param['steps']}, size: {param['width']}×{param['height']}\ndenoise: {param['denoise']}, sampler: {param['sampler']}, sched: {param['scheduler']}\nguidance: {param['guidance']}, max/base shift: {param['max_shift']}/{param['base_shift']}"
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lines = text.split("\n")
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text_height = line_height * len(lines)
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text_image = Image.new('RGB', (width, text_height), color=(0, 0, 0))
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