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smthem
2024-09-08 11:15:55 +08:00
parent 6d1ab0f789
commit 328a2f3ddd
10 changed files with 313 additions and 227 deletions
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Hidiffusion_node.py
+30 -102
View File
@@ -5,7 +5,6 @@ import torch
import os
from PIL import Image
import numpy as np
import sys
from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMScheduler, ControlNetModel,
KDPM2AncestralDiscreteScheduler, LMSDiscreteScheduler,
AutoPipelineForInpainting, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler,
@@ -15,16 +14,17 @@ from diffusers import (StableDiffusionXLPipeline, DiffusionPipeline, DDIMSchedul
EulerAncestralDiscreteScheduler, UniPCMultistepScheduler, AutoencoderKL,
StableDiffusionXLControlNetPipeline, DDPMScheduler, TCDScheduler, LCMScheduler,
StableDiffusionPipeline, StableDiffusionControlNetPipeline, StableDiffusionXLInpaintPipeline)
from diffusers.models.unets import unet_2d_condition
from diffusers.loaders.single_file_utils import load_single_file_checkpoint,infer_diffusers_model_type
from huggingface_hub import hf_hub_download
from .hidiffusion.hidiffusion import apply_hidiffusion,remove_hidiffusion
import folder_paths
from safetensors.torch import load_file, load
from safetensors.torch import load_file
import yaml
import diffusers
import random
from omegaconf import OmegaConf
from comfy.model_management import cleanup_models
from comfy.clip_vision import load as load_clip
dif_version = str(diffusers.__version__)
dif_version_int = int(dif_version.split(".")[1])
@@ -76,7 +76,6 @@ lcm_unet = ["dmd2_sdxl_4step_unet_fp16.bin", "dmd2_sdxl_1step_unet_fp16.bin", "l
"Hyper-SDXL-1step-Unet.safetensors"]
def tensor_to_image(tensor):
#tensor = tensor.cpu()
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
image = Image.fromarray(image_np, mode='RGB')
return image
@@ -161,31 +160,6 @@ def get_sheduler(name):
return scheduler
def get_local_path(file_path, model_path):
path = os.path.join(file_path, "models", "diffusers", model_path)
model_path = os.path.normpath(path)
if sys.platform == 'win32':
model_path = model_path.replace('\\', "/")
return model_path
def get_instance_path(path):
instance_path = os.path.normpath(path)
if sys.platform == 'win32':
instance_path = instance_path.replace('\\', "/")
return instance_path
def instance_path(path, repo):
if repo == "":
if path == "none":
repo = "none"
else:
model_path = get_local_path(file_path, path)
repo = get_instance_path(model_path)
return repo
class HI_Diffusers_Model_Loader:
def __init__(self):
pass
@@ -204,7 +178,9 @@ class HI_Diffusers_Model_Loader:
"trigger_words": ("STRING", {"default": "best quality"}),
"scheduler": (scheduler_list,),
"apply_window_attn":("BOOLEAN", {"default": False},),
"ip_adapter": ("BOOLEAN", {"default": False},),
"ip_ckpt": (["none"] + folder_paths.get_filename_list("photomaker"),),
"clip_vision": (["none"] + folder_paths.get_filename_list("clip_vision"),),
}
}
@@ -215,7 +191,7 @@ class HI_Diffusers_Model_Loader:
CATEGORY = "Hidiffusion_Pro"
def loader_models(self,function_choice, ckpt_name,vae_id,unet_model, controlnet_model,
lora,lora_scale,trigger_words,scheduler,apply_window_attn,ip_adapter):
lora,lora_scale,trigger_words,scheduler,apply_window_attn,ip_ckpt,clip_vision):
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) if ckpt_name!="none" else None
sd_type=""
@@ -223,22 +199,15 @@ class HI_Diffusers_Model_Loader:
sd = load_single_file_checkpoint(ckpt_path)
sd_type = infer_diffusers_model_type(sd)
del sd
vae_id=vae_id if vae_id!="none" else None
controlnet_path=folder_paths.get_full_path("controlnet", controlnet_model) if controlnet_model!="none" else None
unet_ckpt = folder_paths.get_full_path("unet", unet_model) if unet_model!="none" else None
ip_ckpt = folder_paths.get_full_path("photomaker", ip_ckpt) if ip_ckpt != "none" else None
clip_vision = folder_paths.get_full_path("clip_vision", clip_vision) if clip_vision != "none" else None
scheduler_used = get_sheduler(scheduler)
ip_path = os.path.join(folder_paths.models_dir, "photomaker")
sdxl_models_pre = os.path.join(ip_path, "sdxl_models", "image_encoder")
if not os.path.exists(sdxl_models_pre):
os.makedirs(sdxl_models_pre)
sd_models_pre = os.path.join(ip_path, "models", "image_encoder")
if not os.path.exists(sd_models_pre):
os.makedirs(sd_models_pre)
if sd_type == "v1" or sd_type == "v2":
model_type="stable-diffusion-v1-5"
model_config=os.path.join(dir_path,"sd15_config")
@@ -281,7 +250,7 @@ class HI_Diffusers_Model_Loader:
if dif_version_int >= 28:
model = StableDiffusionXLPipeline.from_single_file(
pretrained_model_link_or_path=ckpt_path, config=model_config,original_config=original_config_file, torch_dtype=torch.float16)
ckpt_path, config=model_config,original_config=original_config_file, torch_dtype=torch.float16)
else:
model = StableDiffusionXLPipeline.from_single_file(
ckpt_path,config=model_config, original_config_file=original_config_file, torch_dtype=torch.float16)
@@ -290,6 +259,7 @@ class HI_Diffusers_Model_Loader:
controlnet = ControlNetModel.from_unet(model.unet)
cn_state_dict = load_file(controlnet_path)
controlnet.load_state_dict(cn_state_dict, strict=False)
controlnet.to(torch.float16)
if function_choice == "img2img":
model = StableDiffusionXLControlNetImg2ImgPipeline.from_single_file(ckpt_path,config=model_config,original_config=original_config_file,controlnet=controlnet,
torch_dtype=torch.float16,
@@ -324,7 +294,6 @@ class HI_Diffusers_Model_Loader:
if lora!="none":
lora_path = folder_paths.get_full_path("loras", lora)
lora_path = get_instance_path(lora_path)
model.load_lora_weights(lora_path, adapter_name=trigger_words)
model.fuse_lora(lora_scale=lora_scale, adapter_names=[trigger_words,])
@@ -332,66 +301,25 @@ class HI_Diffusers_Model_Loader:
model.enable_vae_tiling()
apply_hidiffusion(model,apply_window_attn=apply_window_attn,model_type_str=model_type)
model.enable_model_cpu_offload() # need below apply_hidiffusion(model)
if ip_adapter:
ip_adapter = False
if ip_ckpt is not None and clip_vision is not None:
model.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
ip_model_dir_origin = get_instance_path(ip_path)
device = "cuda"
if model_type == "stable-diffusion-xl-base-1.0": # #ip-adapter_sdxl.bin
ip_model_path_xl = os.path.join(ip_path, "sdxl_models", "ip-adapter_sdxl.bin")
if not os.path.exists(ip_model_path_xl):
adapter_path_xl = hf_hub_download(
repo_id="h94/IP-Adapter",
subfolder="sdxl_models",
filename="ip-adapter_sdxl.bin",
local_dir=ip_model_dir_origin,
)
else:
adapter_path_xl = get_instance_path(ip_model_path_xl)
image_encoder_path_xl = get_instance_path(
os.path.join(ip_path, "sdxl_models", "image_encoder", "model.safetensors"))
if not os.path.exists(ip_model_path_xl):
os.makedirs(ip_model_path_xl)
ip_model_path_dir = get_instance_path(os.path.join(ip_path, "sdxl_models", "image_encoder"))
if not os.path.exists(image_encoder_path_xl):
adapter_encoder_xl = hf_hub_download(
repo_id="h94/IP-Adapter",
subfolder="sdxl_models/image_encoder",
local_dir=ip_model_dir_origin,
)
else:
adapter_encoder_xl = get_instance_path(ip_model_path_dir)
model = IPAdapterXL(model, adapter_encoder_xl, adapter_path_xl, device,
target_blocks=["up_blocks.0.attentions.1"])
elif model_type == "stable-diffusion-v1-5": # SD1.5 ip-adapter_sd15.bin
# sd1.5
ip_model_path = os.path.join(ip_path, "models", "ip-adapter_sd15.bin")
if not os.path.exists(ip_model_path):
adapter_path_sd = hf_hub_download(
repo_id="h94/IP-Adapter",
subfolder="models",
filename="ip-adapter_sd15.bin",
local_dir=ip_model_dir_origin,
)
else:
adapter_path_sd = get_instance_path(ip_model_path)
image_encoder_path = get_instance_path(
os.path.join(ip_path, "models", "image_encoder", "model.safetensors"))
ip_model_encoder_dir = get_instance_path(os.path.join(ip_path, "models", "image_encoder"))
if not os.path.exists(image_encoder_path):
adapter_encoder = hf_hub_download(
repo_id="h94/IP-Adapter",
subfolder="models/image_encoder",
filename="model.safetensors",
local_dir=ip_model_dir_origin,
)
else:
adapter_encoder = get_instance_path(ip_model_encoder_dir)
model = IPAdapter(model, adapter_encoder, adapter_path_sd, device, target_blocks=["block"])
image_encoder = load_clip(clip_vision)
if sd_type == "xl_base":
config_path=os.path.join(dir_path,"weights","sdxl","config.json")
image_encoder_config = OmegaConf.load(config_path)
model = IPAdapterXL(model, image_encoder, ip_ckpt, device,image_encoder_config,
target_blocks=["up_blocks.0.attentions.1"])
elif sd_type == "v1":
config_path = os.path.join(dir_path, "weights", "sd15","config.json")
image_encoder_config = OmegaConf.load(config_path)
model = IPAdapter(model, image_encoder, ip_ckpt, device,image_encoder_config, target_blocks=["block"])
else:
raise "unsupport model,only support SDXL or SD1.5"
torch.cuda.empty_cache()
ip_adapter=True
torch.cuda.empty_cache()
pipe={"model":model,"controlnet_path":controlnet_path,"sd_type":sd_type,"lora":lora,"trigger_words":trigger_words,"ip_adapter":ip_adapter,"function_choice":function_choice}
torch.cuda.empty_cache()
@@ -444,7 +372,7 @@ class Hi_Sampler:
if ip_adapter:
ip_image = kwargs.get("ip_image")
ip_image = input_size_adaptation_output(ip_image, pre_input, width, height)
#ip_image = input_size_adaptation_output(ip_image, pre_input, width, height)
if lora != "none":
prompt = prompt + " " + trigger_words
if controlnet_path is None:
README.md
+14 -15
View File
@@ -7,19 +7,22 @@ HiDiffusion From: [link](https://github.com/megvii-research/HiDiffusion)
Update
----
**09/07**
*fix runway error/del repo /auto choice model type
*修复runwaybug / 去掉repo加载模型的方式 /自动选择模型的类别 s
**09/08**
* adapter style now using single file.
* adapter style改成单体文件模式
**Previous updates**
*修复runwaybug / 去掉repo加载模型的方式 /自动选择模型的类别
* 增加adapter_style支持，SDXL需求的显存较大，虽然能跑CPU，但是不推荐，会爆显存，SD1.5测试没问题。
* 增加 manne加速Lora
* 加入controlnet-tile-sdxl的支持，内置图片预处理，默认512尺寸，新增apply_window_attn 条件控制。
* 修复节点连接逻辑，现在文生图模式，无需接入image，无controlnet也无需接入control_image
* 支持SDXL-lighting\Hyper\LCM\DMD2\的加速Unet，建议适当提高步数；
* 基于官方的更新，加入lora支持，需要填关键词；
* 加入skip，去掉意义不大的其他参数；
* 加入skip，去掉意义不大的其他参数；
*fix runway error/del repo /auto choice model type
* Adding adapter_style support, SDXL requires a large amount of graphics memory. Although it can run on CPU, it is not recommended as it may cause graphics memory " explosion". SD1.5 testing is not a problem.
* add manne lighting lora
* Added support for control net file sdxl, built-in image preprocessing, default size of 512, and added condition control for apply_window_attn.
@@ -63,25 +66,21 @@ yaml
| ├──any SDXL lighting Unet
```
3.4 if using adapter style
如果不存放对应，运行时会自动下载。 If the corresponding file is not stored, it will be automatically downloaded at runtime.
```
├── ComfyUI/models/photomaker
| ├──models/
| ├── ip-adapter_sd15.bin
| ├── image_encoder/
| ├── config.json
| ├── model.safetensors
| ├──sdxl_models/
| ├── ip-adapter_sdxl.bin
| ├── image_encoder/
| ├── config.json
| ├── model.safetensors
├── ComfyUI/models/clip_vision
| ├── sdxl_model.safetensors # rename from sd15/ncoder/model.safetensors
| ├── sdm_model.safetensors # rename from sdxl/encoder/model.safetensors
```
4 example
-----
new workflow example 最新的示例
new workflow example new
![](https://github.com/smthemex/ComfyUI_HiDiffusion_Pro/blob/main/example/new.png)
sd1.5 using ip_adapter_style 使用ip_adapter_style
example/controlnet_img2img.png
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example/img2img_lora.png
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example/lightingUnet.png
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example/new.json
+140 -90
View File
@@ -1,32 +1,144 @@
{
"last_node_id": 5,
"last_link_id": 4,
"last_node_id": 9,
"last_link_id": 10,
"nodes": [
{
"id": 5,
"id": 3,
"type": "SaveImage",
"pos": [
4311,
-264
],
"size": {
"0": 367.2918701171875,
"1": 381.46820068359375
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 9,
"label": "images"
}
],
"properties": {},
"widgets_values": [
"ComfyUI"
]
},
{
"id": 6,
"type": "LoadImage",
"pos": [
3473,
10
],
"size": {
"0": 315,
"1": 314
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
10
],
"shape": 3,
"label": "IMAGE",
"slot_index": 0
},
{
"name": "MASK",
"type": "MASK",
"links": null,
"shape": 3,
"label": "MASK"
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"4.jpg",
"image"
]
},
{
"id": 8,
"type": "HI_Diffusers_Model_Loader",
"pos": [
3472,
-365
],
"size": {
"0": 315,
"1": 322
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "pipe",
"type": "HIDIF_MODEL",
"links": [
7
],
"slot_index": 0,
"shape": 3,
"label": "pipe"
}
],
"properties": {
"Node name for S&R": "HI_Diffusers_Model_Loader"
},
"widgets_values": [
"img2img",
"0SDXL\\juggernautXL_v8Rundiffusion.safetensors",
"none",
"sdxl_lightning_4step_unet.safetensors",
"none",
"none",
0.8,
"best quality",
"Euler",
false,
"none",
"none"
]
},
{
"id": 9,
"type": "Hi_Sampler",
"pos": [
3915,
-197
3875,
-292
],
"size": {
"0": 400,
"1": 426
},
"flags": {},
"order": 1,
"order": 2,
"mode": 0,
"inputs": [
{
"name": "pipe",
"type": "HIDIF_MODEL",
"link": 3,
"link": 7,
"label": "pipe"
},
{
"name": "image",
"type": "IMAGE",
"link": null,
"link": 10,
"label": "image"
},
{
@@ -47,7 +159,7 @@
"name": "image",
"type": "IMAGE",
"links": [
4
9
],
"slot_index": 0,
"shape": 3,
@@ -63,112 +175,50 @@
0.5,
1,
512,
635048020868939,
1108821181920656,
"randomize",
30,
7.5,
12,
3,
2048,
2048,
1
]
},
{
"id": 4,
"type": "HI_Diffusers_Model_Loader",
"pos": [
3512,
-184
],
"size": [
366.36151183893753,
307.7217713270144
],
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "pipe",
"type": "HIDIF_MODEL",
"links": [
3
],
"slot_index": 0,
"shape": 3,
"label": "pipe"
}
],
"properties": {
"Node name for S&R": "HI_Diffusers_Model_Loader"
},
"widgets_values": [
"txt2img",
"0SDXL\\animagineXLV3_v30.safetensors",
"none",
"none",
"none",
"none",
0.8,
"best quality",
"DDIM",
false,
false
]
},
{
"id": 3,
"type": "SaveImage",
"pos": [
4336,
-177
],
"size": [
367.29185650870204,
381.4682013986403
],
"flags": {},
"order": 2,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 4,
"label": "images"
}
],
"properties": {},
"widgets_values": [
"ComfyUI"
]
}
],
"links": [
[
3,
4,
7,
8,
0,
5,
9,
0,
"HIDIF_MODEL"
],
[
4,
5,
9,
9,
0,
3,
0,
"IMAGE"
],
[
10,
6,
0,
9,
1,
"IMAGE"
]
],
"groups": [],
"config": {},
"extra": {
"ds": {
"scale": 1.283902517749519,
"scale": 1.0610764609500176,
"offset": [
-3462.543452344591,
367.22629242211144
-3283.5070365830798,
461.2881609587324
]
}
},
example/sd15ipstyle.png
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ip_adapter/ip_adapter.py
+25 -20
View File
@@ -9,7 +9,7 @@ from safetensors import safe_open
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from .utils import is_torch2_available, get_generator
from comfy.model_management import cleanup_models
if is_torch2_available():
from .attention_processor import (
AttnProcessor2_0 as AttnProcessor,
@@ -64,20 +64,22 @@ class MLPProjModel(torch.nn.Module):
class IPAdapter:
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, target_blocks=["block"]):
def __init__(self, sd_pipe, image_encoder, ip_ckpt, device,image_encoder_config, num_tokens=4, target_blocks=["block"]):
self.device = device
self.image_encoder_path = image_encoder_path
# self.image_encoder_path = image_encoder_path
self.ip_ckpt = ip_ckpt
self.num_tokens = num_tokens
self.target_blocks = target_blocks
self.image_encoder_config=image_encoder_config
self.pipe = sd_pipe.to(self.device)
self.set_ip_adapter()
# load image encoder
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
self.device, dtype=torch.float16
)
# self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
# self.device, dtype=torch.float16
# )
self.image_encoder = image_encoder.encode_image
self.clip_image_processor = CLIPImageProcessor()
# image proj model
self.image_proj_model = self.init_proj()
@@ -87,7 +89,7 @@ class IPAdapter:
def init_proj(self):
image_proj_model = ImageProjModel(
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
clip_embeddings_dim=self.image_encoder.config.projection_dim,
clip_embeddings_dim=self.image_encoder_config["projection_dim"],
clip_extra_context_tokens=self.num_tokens,
).to(self.device, dtype=torch.float16)
return image_proj_model
@@ -153,17 +155,19 @@ class IPAdapter:
@torch.inference_mode()
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
if pil_image is not None:
if isinstance(pil_image, Image.Image):
pil_image = [pil_image]
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
if isinstance(pil_image, torch.Tensor):
clip_image_embeds = self.image_encoder(pil_image)["image_embeds"]
clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
del self.image_encoder
cleanup_models(keep_clone_weights_loaded=False)
else:
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
if content_prompt_embeds is not None:
clip_image_embeds = clip_image_embeds - content_prompt_embeds
clip_image_embeds = clip_image_embeds.clone().detach().to(self.device, dtype=torch.float16)
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
return image_prompt_embeds, uncond_image_prompt_embeds
@@ -188,9 +192,9 @@ class IPAdapter:
**kwargs,
):
self.set_scale(scale)
if pil_image is not None:
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
d1, _, _, _ = pil_image.size()
if isinstance(pil_image,torch.Tensor) :
num_prompts = d1
else:
num_prompts = clip_image_embeds.size(0)
@@ -256,8 +260,8 @@ class IPAdapterXL(IPAdapter):
**kwargs,
):
self.set_scale(scale)
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
d1,_,_,_=pil_image.size()
num_prompts = d1
if prompt is None:
prompt = "best quality, high quality"
@@ -313,6 +317,7 @@ class IPAdapterXL(IPAdapter):
self.generator = get_generator(seed, self.device)
cleanup_models(keep_clone_weights_loaded=False)
images = self.pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
weights/sd15/config.json
+23
View File
@@ -0,0 +1,23 @@
{
"_name_or_path": "./image_encoder",
"architectures": [
"CLIPVisionModelWithProjection"
],
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "gelu",
"hidden_size": 1280,
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-05,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 32,
"patch_size": 14,
"projection_dim": 1024,
"torch_dtype": "float16",
"transformers_version": "4.28.0.dev0"
}
weights/sdxl/config.json
+81
View File
@@ -0,0 +1,81 @@
{
"architectures": [
"CLIPVisionModelWithProjection"
],
"_name_or_path": "",
"add_cross_attention": false,
"architectures_": null,
"attention_dropout": 0.0,
"bad_words_ids": null,
"begin_suppress_tokens": null,
"bos_token_id": null,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0.0,
"do_sample": false,
"dropout": 0.0,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": null,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "gelu",
"hidden_size": 1664,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 8192,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-05,
"length_penalty": 1.0,
"max_length": 20,
"min_length": 0,
"model_type": "clip_vision_model",
"no_repeat_ngram_size": 0,
"num_attention_heads": 16,
"num_beam_groups": 1,
"num_beams": 1,
"num_channels": 3,
"num_hidden_layers": 48,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": null,
"patch_size": 14,
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"suppress_tokens": null,
"task_specific_params": null,
"temperature": 1.0,
"tf_legacy_loss": false,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": null,
"torchscript": false,
"transformers_version": "4.24.0",
"typical_p": 1.0,
"use_bfloat16": false,
"projection_dim": 1280
}