module diffusers has no attribute LCMScheduler

[moosescap]

The Python environment and ConfyUI have been updated to the latest versions. If you need to make changes to the code, please provide detailed instructions, as I am not proficient in programming.

Error occurred when executing LCM_Sampler:

module diffusers has no attribute LCMScheduler

File "F:\comfui\ComfyUI\ComfyUI\execution.py", line 153, in recursive_execute output_data, output_ui = get_output_data(obj, input_data_all) File "F:\comfui\ComfyUI\ComfyUI\execution.py", line 83, in get_output_data return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True) File "F:\comfui\ComfyUI\ComfyUI\execution.py", line 76, in map_node_over_list results.append(getattr(obj, func)(**slice_dict(input_data_all, i))) File "F:\comfui\ComfyUI\ComfyUI\custom_nodes\ComfyUI-LCM\nodes.py", line 47, in sample self.pipe = LatentConsistencyModelPipeline.from_pretrained( File "F:\comfui\ComfyUI\python_embeded\lib\site-packages\diffusers\pipelines\pipeline_utils.py", line 1098, in from_pretrained maybe_raise_or_warn( File "F:\comfui\ComfyUI\python_embeded\lib\site-packages\diffusers\pipelines\pipeline_utils.py", line 282, in maybe_raise_or_warn class_obj = getattr(library, class_name) File "F:\comfui\ComfyUI\python_embeded\lib\site-packages\diffusers\utils\import_utils.py", line 677, in getattr raise AttributeError(f"module {self.name} has no attribute {name}")

[danila-pryamonosov-hypemasters]

adding this lines fixes this iussue and make it to use old pipeline

self.pipe = LatentConsistencyModelPipeline.from_pretrained(
    ...
    custom_revision="main",
    revision="fb9c5d",
    ...
)

[moosescap]

adding this lines fixes this iussue and make it to use old pipeline添加此行可以修复此 iussue 并使其使用旧管道

self.pipe = LatentConsistencyModelPipeline.from_pretrained(
    ...
    custom_revision="main",
    revision="fb9c5d",
    ...
)

thank you!!

[SwannSchilling]

I am having the same problem, could you please provide more info?

The code you provided is for nodes.py? I tried to fix it but it did not work, so could you post the full code please... I found this two times

def sample(self, seed, steps, prompt_strength, cfg, images, conditioning, height, width, num_images, use_fp16):
        if self.pipe is None:
            self.pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
                pretrained_model_name_or_path="SimianLuo/LCM_Dreamshaper_v7",
                safety_checker=None,
            )

            if use_fp16:
                self.pipe.to(torch_device=get_torch_device(),
                             torch_dtype=torch.float16)
            else:
                self.pipe.to(torch_device=get_torch_device(),
                             torch_dtype=torch.float32)

[danila-pryamonosov-hypemasters]

Shure, here are full example for img2img advanced

class LCM_img2img_Sampler_Advanced:
    def __init__(self):
        self.scheduler = LCMScheduler.from_pretrained(
            path.join(path.dirname(__file__), "scheduler_config.json"))
        self.pipe = None

    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                {
                    "images": ("IMAGE", ),
                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "prompt_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
                    "steps": ("INT", {"default": 4, "min": 1, "max": 10000}),
                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.5, "round": 0.01}),
                    "height": ("INT", {"default": 512, "min": 512, "max": 2048}),
                    "width": ("INT", {"default": 512, "min": 512, "max": 2048}),
                    "num_images": ("INT", {"default": 1, "min": 1, "max": 64}),
                    "use_fp16": ("BOOLEAN", {"default": True}),
                    "conditioning": ("CONDITIONING",),
                }
                }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "sample"
    CATEGORY = "sampling"

    def sample(self, seed, steps, prompt_strength, cfg, images, conditioning, height, width, num_images, use_fp16):
        if self.pipe is None:
            self.pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained(
                pretrained_model_name_or_path="SimianLuo/LCM_Dreamshaper_v7",
                custom_revision="main",
                revision="fb9c5d",
                safety_checker=None,
            )

            if use_fp16:
                self.pipe.to(torch_device=get_torch_device(),
                             torch_dtype=torch.float16)
            else:
                self.pipe.to(torch_device=get_torch_device(),
                             torch_dtype=torch.float32)

        torch.manual_seed(seed)
        start_time = time.time()

        images = np.transpose(images, (0, 3, 1, 2))
        results = []
        for i in range(images.shape[0]):
            image = images[i]
            result = self.pipe(
                image=image,
                prompt_embeds=conditioning[0][0],
                strength=prompt_strength,
                width=width,
                height=height,
                guidance_scale=cfg,
                num_inference_steps=steps,
                num_images_per_prompt=num_images,
                lcm_origin_steps=50,
                output_type="np",
                ).images

            # scale latents with vae factor
            tensor_results = [torch.from_numpy(np_result) for np_result in result]
            results.extend(tensor_results)

        results = torch.stack(results)

        print("LCM img2img inference time: ", time.time() - start_time, "seconds")

        return (results,)

[danila-pryamonosov-hypemasters]

As you can see its just need to add two arguments

custom_revision="main", revision="fb9c5d", to self.pipe = LatentConsistencyModelImg2ImgPipeline.from_pretrained()

this also need to be addet to text2image,text2imageadvanced ans so one

[SwannSchilling]

Thanks a lot for the quick reply! Got it to work now! I would not have been able to figure it out by myself...