mirror of
https://github.com/kohya-ss/sd-scripts.git
synced 2026-04-09 06:45:09 +00:00
fix NaN in sampling image
This commit is contained in:
Kohya S
@@ -922,7 +922,7 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
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if up1 is not None:
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uncond_pool = up1
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dtype = text_embeddings_list[0].dtype
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dtype = self.unet.dtype
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# 4. Preprocess image and mask
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if isinstance(image, PIL.Image.Image):
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@@ -3874,127 +3874,127 @@ def sample_images_common(
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cuda_rng_state = torch.cuda.get_rng_state() if torch.cuda.is_available() else None
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with torch.no_grad():
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with accelerator.autocast():
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for i, prompt in enumerate(prompts):
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if not accelerator.is_main_process:
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continue
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# with accelerator.autocast():
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for i, prompt in enumerate(prompts):
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if not accelerator.is_main_process:
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continue
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if isinstance(prompt, dict):
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negative_prompt = prompt.get("negative_prompt")
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sample_steps = prompt.get("sample_steps", 30)
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width = prompt.get("width", 512)
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height = prompt.get("height", 512)
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scale = prompt.get("scale", 7.5)
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seed = prompt.get("seed")
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controlnet_image = prompt.get("controlnet_image")
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prompt = prompt.get("prompt")
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else:
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# prompt = prompt.strip()
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# if len(prompt) == 0 or prompt[0] == "#":
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# continue
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if isinstance(prompt, dict):
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negative_prompt = prompt.get("negative_prompt")
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sample_steps = prompt.get("sample_steps", 30)
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width = prompt.get("width", 512)
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height = prompt.get("height", 512)
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scale = prompt.get("scale", 7.5)
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seed = prompt.get("seed")
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controlnet_image = prompt.get("controlnet_image")
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prompt = prompt.get("prompt")
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else:
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# prompt = prompt.strip()
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# if len(prompt) == 0 or prompt[0] == "#":
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# continue
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# subset of gen_img_diffusers
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prompt_args = prompt.split(" --")
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prompt = prompt_args[0]
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negative_prompt = None
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sample_steps = 30
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width = height = 512
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scale = 7.5
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seed = None
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controlnet_image = None
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for parg in prompt_args:
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try:
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m = re.match(r"w (\d+)", parg, re.IGNORECASE)
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if m:
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width = int(m.group(1))
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continue
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m = re.match(r"h (\d+)", parg, re.IGNORECASE)
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if m:
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height = int(m.group(1))
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continue
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m = re.match(r"d (\d+)", parg, re.IGNORECASE)
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if m:
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seed = int(m.group(1))
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continue
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m = re.match(r"s (\d+)", parg, re.IGNORECASE)
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if m: # steps
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sample_steps = max(1, min(1000, int(m.group(1))))
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continue
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m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
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if m: # scale
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scale = float(m.group(1))
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continue
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m = re.match(r"n (.+)", parg, re.IGNORECASE)
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if m: # negative prompt
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negative_prompt = m.group(1)
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continue
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m = re.match(r"cn (.+)", parg, re.IGNORECASE)
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if m: # negative prompt
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controlnet_image = m.group(1)
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continue
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except ValueError as ex:
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print(f"Exception in parsing / 解析エラー: {parg}")
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print(ex)
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if seed is not None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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if prompt_replacement is not None:
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prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
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if negative_prompt is not None:
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negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
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if controlnet_image is not None:
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controlnet_image = Image.open(controlnet_image).convert("RGB")
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controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
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height = max(64, height - height % 8) # round to divisible by 8
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width = max(64, width - width % 8) # round to divisible by 8
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print(f"prompt: {prompt}")
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print(f"negative_prompt: {negative_prompt}")
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print(f"height: {height}")
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print(f"width: {width}")
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print(f"sample_steps: {sample_steps}")
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print(f"scale: {scale}")
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image = pipeline(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=sample_steps,
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guidance_scale=scale,
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negative_prompt=negative_prompt,
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controlnet=controlnet,
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controlnet_image=controlnet_image,
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).images[0]
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ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
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num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
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seed_suffix = "" if seed is None else f"_{seed}"
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img_filename = (
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f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
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)
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image.save(os.path.join(save_dir, img_filename))
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# wandb有効時のみログを送信
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try:
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wandb_tracker = accelerator.get_tracker("wandb")
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# subset of gen_img_diffusers
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prompt_args = prompt.split(" --")
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prompt = prompt_args[0]
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negative_prompt = None
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sample_steps = 30
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width = height = 512
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scale = 7.5
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seed = None
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controlnet_image = None
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for parg in prompt_args:
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try:
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import wandb
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except ImportError: # 事前に一度確認するのでここはエラー出ないはず
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raise ImportError("No wandb / wandb がインストールされていないようです")
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m = re.match(r"w (\d+)", parg, re.IGNORECASE)
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if m:
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width = int(m.group(1))
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continue
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wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
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except: # wandb 無効時
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pass
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m = re.match(r"h (\d+)", parg, re.IGNORECASE)
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if m:
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height = int(m.group(1))
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continue
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m = re.match(r"d (\d+)", parg, re.IGNORECASE)
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if m:
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seed = int(m.group(1))
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continue
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m = re.match(r"s (\d+)", parg, re.IGNORECASE)
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if m: # steps
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sample_steps = max(1, min(1000, int(m.group(1))))
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continue
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m = re.match(r"l ([\d\.]+)", parg, re.IGNORECASE)
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if m: # scale
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scale = float(m.group(1))
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continue
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m = re.match(r"n (.+)", parg, re.IGNORECASE)
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if m: # negative prompt
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negative_prompt = m.group(1)
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continue
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m = re.match(r"cn (.+)", parg, re.IGNORECASE)
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if m: # negative prompt
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controlnet_image = m.group(1)
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continue
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except ValueError as ex:
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print(f"Exception in parsing / 解析エラー: {parg}")
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print(ex)
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if seed is not None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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if prompt_replacement is not None:
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prompt = prompt.replace(prompt_replacement[0], prompt_replacement[1])
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if negative_prompt is not None:
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negative_prompt = negative_prompt.replace(prompt_replacement[0], prompt_replacement[1])
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if controlnet_image is not None:
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controlnet_image = Image.open(controlnet_image).convert("RGB")
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controlnet_image = controlnet_image.resize((width, height), Image.LANCZOS)
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height = max(64, height - height % 8) # round to divisible by 8
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width = max(64, width - width % 8) # round to divisible by 8
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print(f"prompt: {prompt}")
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print(f"negative_prompt: {negative_prompt}")
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print(f"height: {height}")
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print(f"width: {width}")
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print(f"sample_steps: {sample_steps}")
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print(f"scale: {scale}")
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image = pipeline(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=sample_steps,
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guidance_scale=scale,
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negative_prompt=negative_prompt,
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controlnet=controlnet,
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controlnet_image=controlnet_image,
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).images[0]
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ts_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
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num_suffix = f"e{epoch:06d}" if epoch is not None else f"{steps:06d}"
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seed_suffix = "" if seed is None else f"_{seed}"
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img_filename = (
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f"{'' if args.output_name is None else args.output_name + '_'}{ts_str}_{num_suffix}_{i:02d}{seed_suffix}.png"
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)
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image.save(os.path.join(save_dir, img_filename))
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# wandb有効時のみログを送信
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try:
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wandb_tracker = accelerator.get_tracker("wandb")
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try:
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import wandb
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except ImportError: # 事前に一度確認するのでここはエラー出ないはず
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raise ImportError("No wandb / wandb がインストールされていないようです")
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wandb_tracker.log({f"sample_{i}": wandb.Image(image)})
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except: # wandb 無効時
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pass
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# clear pipeline and cache to reduce vram usage
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del pipeline
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