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✨ feat(example): add QR code example

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Jianqi Pan
2024-04-18 01:25:51 +09:00
parent d3edc4a8f4
commit f3c696d5db
4 changed files with 91 additions and 17 deletions
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README.md
+4
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@@ -60,6 +60,10 @@ A checkpoint for stablediffusion 1.5 is all your need. But for full automation,
![Change Clothes](./examples/change_clothes.png)
## QR Code
![QR Code](./examples/qr_code.png)
## FAQ
### Why Diffusers?
__init__.py
+73 -13
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@@ -74,8 +74,8 @@ def latents_to_img_tensor(pipeline, latents):
# 1. 输入的 latents 是一个 -1 ~ 1 之间的 tensor
# 2. 先进行缩放
scaled_latents = latents / pipeline.vae.config.scaling_factor
# 转成 unet 类型
scaled_latents = scaled_latents.to(dtype=comfy.model_management.unet_dtype())
# 转成 vae 类型
scaled_latents = scaled_latents.to(dtype=comfy.model_management.vae_dtype())
# 3. 解码，返回的是 -1 ~ 1 之间的 tensor
dec_tensor = pipeline.vae.decode(scaled_latents, return_dict=False)[0]
# 4. 缩放到 0 ~ 1 之间
@@ -285,6 +285,7 @@ class DiffusersTextureInversionLoader:
path = folder_paths.get_full_path("embeddings", texture_inversion)
token = texture_inversion.split(".")[0]
pipeline.load_textual_inversion(path, token=token)
print(f"Loaded {texture_inversion}")
return (pipeline,)
@@ -351,7 +352,7 @@ class GetAverageColorFromImage:
return max(color_counts, key=color_counts.get)
class DiffusersPipeline:
class DiffusersXLPipeline:
CATEGORY = "Jannchie"
FUNCTION = "run"
RETURN_TYPES = ("DIFFUSERS_PIPELINE",)
@@ -361,7 +362,7 @@ class DiffusersPipeline:
def INPUT_TYPES(cls):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
"ckpt_name": (["playgroundai/playground-v2.5-1024px-aesthetic"],),
},
"optional": {
"vae_name": (
@@ -379,6 +380,54 @@ class DiffusersPipeline:
def run(self, ckpt_name: str, vae_name: str = None, scheduler_name: str = None):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
if ckpt_path is None:
ckpt_path = ckpt_name
if vae_name == "-":
vae_path = None
else:
vae_path = folder_paths.get_full_path("vae", vae_name)
if scheduler_name == "-":
scheduler_name = None
self.pipeline_wrapper = PipelineWrapper(
ckpt_path, vae_path, scheduler_name, pipeline=StableDiffusionPipeline
)
return (self.pipeline_wrapper.pipeline,)
class DiffusersPipeline:
CATEGORY = "Jannchie"
FUNCTION = "run"
RETURN_TYPES = ("DIFFUSERS_PIPELINE",)
RETURN_NAMES = ("pipeline",)
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"ckpt_name": (
["playgroundai/playground-v2.5-1024px-aesthetic"]
+ folder_paths.get_filename_list("checkpoints"),
),
},
"optional": {
"vae_name": (
folder_paths.get_filename_list("vae") + ["-"],
{"default": "-"},
),
"scheduler_name": (
list(schedulers.keys()) + ["-"],
{
"default": "-",
},
),
},
}
def run(self, ckpt_name: str, vae_name: str = None, scheduler_name: str = None):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
if ckpt_path is None:
ckpt_path = ckpt_name
if vae_name == "-":
vae_path = None
else:
@@ -477,6 +526,7 @@ controlnet_list = [
"seg",
"softedge",
"lineart_anime",
"other",
]
@@ -492,18 +542,26 @@ class DiffusersControlNetLoader:
"required": {
"controlnet_model_name": (controlnet_list,),
},
"optional": {
"controlnet_model_file": (folder_paths.get_filename_list("controlnet"),)
},
}
def run(self, controlnet_model_name: str):
def run(self, controlnet_model_name: str, controlnet_model_file: str = ""):
file_list = folder_paths.get_filename_list("controlnet")
controlnet_model_path = next(
(
folder_paths.get_full_path("controlnet", file)
for file in file_list
if f"_v11p_sd15_{controlnet_model_name}.pth" in file
),
None,
)
if controlnet_model_name == "other":
controlnet_model_path = folder_paths.get_full_path(
"controlnet", controlnet_model_file
)
else:
controlnet_model_path = next(
(
folder_paths.get_full_path("controlnet", file)
for file in file_list
if f"_v11p_sd15_{controlnet_model_name}.pth" in file
),
None,
)
if controlnet_model_path is None:
controlnet_model_path = f"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_{controlnet_model_name}.pth"
controlnet = ControlNetModel.from_single_file(
@@ -780,6 +838,7 @@ NODE_CLASS_MAPPINGS = {
"GetFilledColorImage": GetFilledColorImage,
"GetAverageColorFromImage": GetAverageColorFromImage,
"DiffusersPipeline": DiffusersPipeline,
"DiffusersXLPipeline": DiffusersXLPipeline,
"DiffusersGenerator": DiffusersGenerator,
"DiffusersPrepareLatents": DiffusersPrepareLatents,
"DiffusersDecoder": DiffusersDecoder,
@@ -793,6 +852,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"GetFilledColorImage": "Get Filled Color Image Jannchie",
"GetAverageColorFromImage": "Get Average Color From Image Jannchie",
"DiffusersPipeline": "🤗 Diffusers Pipeline",
"DiffusersXLPipeline": "🤗 Diffusers XL Pipeline",
"DiffusersGenerator": "🤗 Diffusers Generator",
"DiffusersPrepareLatents": "🤗 Diffusers Prepare Latents",
"DiffusersDecoder": "🤗 Diffusers Decoder",
examples/qr_code.png
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pipelines/jannchie.py
+14 -4
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@@ -443,7 +443,7 @@ class JannchiePipeline(StableDiffusionControlNetPipeline):
if mask_image is not None:
mask_condition = self.mask_processor.preprocess(
mask_image, height=height, width=width
)
).to(device=device)
init_image = image
init_image = init_image.to(dtype=torch.float32)
if masked_image_latents is None:
@@ -564,6 +564,7 @@ class JannchiePipeline(StableDiffusionControlNetPipeline):
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
added_cond_kwargs={},
)
self.unet.ref_data.MODE = "read"
@@ -605,7 +606,9 @@ class JannchiePipeline(StableDiffusionControlNetPipeline):
)
if n_controlnet_unit != 0:
down_block_res_samples, mid_block_res_sample = self.controlnet(
control_model_input,
control_model_input.to(
device=device, dtype=self.controlnet.dtype
),
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=controlnet_images,
@@ -632,12 +635,17 @@ class JannchiePipeline(StableDiffusionControlNetPipeline):
down_block_res_samples, mid_block_res_sample = None, None
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
latent_model_input.to(device=device, dtype=self.unet.dtype),
t,
encoder_hidden_states=prompt_embeds,
encoder_hidden_states=prompt_embeds.to(
device=device, dtype=self.unet.dtype
),
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs={
"text_embeds": prompt_embeds,
},
)["sample"]
# perform guidance
if do_classifier_free_guidance:
@@ -983,6 +991,8 @@ class JannchiePipeline(StableDiffusionControlNetPipeline):
)
if return_image_latents or (latents is None and not is_strength_max):
# TODO: check it
if image is None:
image = torch.randn(shape, device=device, dtype=dtype)
image = image.to(device=device, dtype=dtype)
if image.shape[1] == 4: