asd

README.md

@@ -1,78 +0,0 @@
----
-language:
-- en
-tags:
-- stable-diffusion
-- text-to-image
-license: creativeml-openrail-m
-inference: true
-
----
-
-![image](photo_2023-01-06_00-30-33.jpg)
-<sub>pink hair guy in glasses, photograph, sporty body, cinematic lighting, clear eyes, perfect face, blush,  beautiful nose, beautiful eyes, detailed eyes</sub>
-
-
-# Anime Diffusion
-
-Anime Diffusion is a latent text-to-image diffusion model trained on BLIP captions for Danbou set + Demon slayer + arts from 4ch
-
-
-
-## Model Description
-
-## License
-
-This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
-The CreativeML OpenRAIL License specifies: 
-
-1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 
-2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
-3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
-[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
-
-## Downstream Uses
-
-This model can be used for entertainment purposes and as a generative art assistant.
-
-## Example Code
-
-```python
-import torch
-from torch import autocast
-from diffusers import StableDiffusionPipeline
-
-pipe = StableDiffusionPipeline.from_pretrained(
-    'AlexWortega/AnimeDiffuion',
-    torch_dtype=torch.float32
-).to('cuda')
-
-negative_prompt = """low-res, duplicate, poorly drawn face, ugly, undetailed face"""
-d = 'white girl'
-prompt = f"pink hair guy in glasses, photograph, sporty body, cinematic lighting, clear eyes, perfect face, blush,  beautiful nose, beautiful eyes, detailed eyes" 
-num_samples = 1
-
-with  torch.inference_mode():
-    images = pipe([prompt] * num_samples,
-                  negative_prompt = [negative_prompt]*num_samples,
-                  height=512, width=512,
-                  num_inference_steps=50,
-                  guidance_scale=8,
-                  ).images
-
-
-    
-images[0].save("test.png")
-```
-
-## Team Member and Acknowledgements
-
-This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
-
-- [Alex Wortega](https://github.com/AlexWortega)
-<sub>alexwortega@yandex.ru</sub>
-
-
-In order to reach me, here is my blog:
-
-[![My tg channel]](https://t.me/lovedeathtransformers)

text_encoder/config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "_name_or_path": "22h/vintedois-diffusion-v0-1",
   "architectures": [
     "CLIPTextModel"
   ],

text_encoder/pytorch_model.bin

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tokenizer/tokenizer_config.json

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   },
   "errors": "replace",
   "model_max_length": 77,
-  "name_or_path": "runwayml/stable-diffusion-v1-5",
+  "name_or_path": "22h/vintedois-diffusion-v0-1",
   "pad_token": "<|endoftext|>",
   "special_tokens_map_file": "./special_tokens_map.json",
   "tokenizer_class": "CLIPTokenizer",

unet/config.json

@@ -1,7 +1,7 @@
 {
   "_class_name": "UNet2DConditionModel",
   "_diffusers_version": "0.9.0",
-  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "_name_or_path": "22h/vintedois-diffusion-v0-1",
   "act_fn": "silu",
   "attention_head_dim": 8,
   "block_out_channels": [
@@ -11,6 +11,7 @@
     1280
   ],
   "center_input_sample": false,
+  "class_embed_type": null,
   "cross_attention_dim": 768,
   "down_block_types": [
     "CrossAttnDownBlock2D",
@@ -25,11 +26,13 @@
   "in_channels": 4,
   "layers_per_block": 2,
   "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
   "norm_eps": 1e-05,
   "norm_num_groups": 32,
   "num_class_embeds": null,
   "only_cross_attention": false,
   "out_channels": 4,
+  "resnet_time_scale_shift": "default",
   "sample_size": 64,
   "up_block_types": [
     "UpBlock2D",
@@ -37,5 +40,6 @@
     "CrossAttnUpBlock2D",
     "CrossAttnUpBlock2D"
   ],
+  "upcast_attention": false,
   "use_linear_projection": false
 }

unet/diffusion_pytorch_model.bin

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vae/config.json

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 {
   "_class_name": "AutoencoderKL",
   "_diffusers_version": "0.9.0",
-  "_name_or_path": "runwayml/stable-diffusion-v1-5",
+  "_name_or_path": "22h/vintedois-diffusion-v0-1",
   "act_fn": "silu",
   "block_out_channels": [
     128,
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   "layers_per_block": 2,
   "norm_num_groups": 32,
   "out_channels": 3,
-  "sample_size": 512,
+  "sample_size": 256,
   "up_block_types": [
     "UpDecoderBlock2D",
     "UpDecoderBlock2D",

vae/diffusion_pytorch_model.bin

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