g8nz commited on
Commit
9ab14a6
1 Parent(s): 5af7d48
.gitattributes CHANGED
@@ -25,7 +25,6 @@
25
  *.safetensors filter=lfs diff=lfs merge=lfs -text
26
  saved_model/**/* filter=lfs diff=lfs merge=lfs -text
27
  *.tar.* filter=lfs diff=lfs merge=lfs -text
28
- *.tar filter=lfs diff=lfs merge=lfs -text
29
  *.tflite filter=lfs diff=lfs merge=lfs -text
30
  *.tgz filter=lfs diff=lfs merge=lfs -text
31
  *.wasm filter=lfs diff=lfs merge=lfs -text
 
25
  *.safetensors filter=lfs diff=lfs merge=lfs -text
26
  saved_model/**/* filter=lfs diff=lfs merge=lfs -text
27
  *.tar.* filter=lfs diff=lfs merge=lfs -text
 
28
  *.tflite filter=lfs diff=lfs merge=lfs -text
29
  *.tgz filter=lfs diff=lfs merge=lfs -text
30
  *.wasm filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1 +1,193 @@
1
- Copy of stabilityai/stable-diffusion-x4-upscaler.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: openrail++
3
+ tags:
4
+ - stable-diffusion
5
+ inference: false
6
+ ---
7
+
8
+ # Stable Diffusion x4 upscaler model card
9
+ This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion).
10
+ This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
11
+ In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
12
+
13
+ ![Image](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/upscaling/merged-dog.png)
14
+
15
+ - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `x4-upscaler-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.ckpt).
16
+ - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler#examples)
17
+
18
+
19
+ ## Model Details
20
+ - **Developed by:** Robin Rombach, Patrick Esser
21
+ - **Model type:** Diffusion-based text-to-image generation model
22
+ - **Language(s):** English
23
+ - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
24
+ - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
25
+ - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
26
+ - **Cite as:**
27
+
28
+ @InProceedings{Rombach_2022_CVPR,
29
+ author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
30
+ title = {High-Resolution Image Synthesis With Latent Diffusion Models},
31
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
32
+ month = {June},
33
+ year = {2022},
34
+ pages = {10684-10695}
35
+ }
36
+
37
+
38
+ ## Examples
39
+
40
+ Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
41
+
42
+ ```bash
43
+ pip install diffusers transformers accelerate scipy safetensors
44
+ ```
45
+
46
+ ```python
47
+ import requests
48
+ from PIL import Image
49
+ from io import BytesIO
50
+ from diffusers import StableDiffusionUpscalePipeline
51
+ import torch
52
+
53
+ # load model and scheduler
54
+ model_id = "stabilityai/stable-diffusion-x4-upscaler"
55
+ pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
56
+ pipeline = pipeline.to("cuda")
57
+
58
+ # let's download an image
59
+ url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
60
+ response = requests.get(url)
61
+ low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
62
+ low_res_img = low_res_img.resize((128, 128))
63
+
64
+ prompt = "a white cat"
65
+
66
+ upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
67
+ upscaled_image.save("upsampled_cat.png")
68
+ ```
69
+
70
+ **Notes**:
71
+ - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
72
+ - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
73
+
74
+
75
+ # Uses
76
+
77
+ ## Direct Use
78
+ The model is intended for research purposes only. Possible research areas and tasks include
79
+
80
+ - Safe deployment of models which have the potential to generate harmful content.
81
+ - Probing and understanding the limitations and biases of generative models.
82
+ - Generation of artworks and use in design and other artistic processes.
83
+ - Applications in educational or creative tools.
84
+ - Research on generative models.
85
+
86
+ Excluded uses are described below.
87
+
88
+ ### Misuse, Malicious Use, and Out-of-Scope Use
89
+ _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
90
+
91
+ The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
92
+
93
+ #### Out-of-Scope Use
94
+ The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
95
+
96
+ #### Misuse and Malicious Use
97
+ Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
98
+
99
+ - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
100
+ - Intentionally promoting or propagating discriminatory content or harmful stereotypes.
101
+ - Impersonating individuals without their consent.
102
+ - Sexual content without consent of the people who might see it.
103
+ - Mis- and disinformation
104
+ - Representations of egregious violence and gore
105
+ - Sharing of copyrighted or licensed material in violation of its terms of use.
106
+ - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
107
+
108
+ ## Limitations and Bias
109
+
110
+ ### Limitations
111
+
112
+ - The model does not achieve perfect photorealism
113
+ - The model cannot render legible text
114
+ - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
115
+ - Faces and people in general may not be generated properly.
116
+ - The model was trained mainly with English captions and will not work as well in other languages.
117
+ - The autoencoding part of the model is lossy
118
+ - The model was trained on a subset of the large-scale dataset
119
+ [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
120
+
121
+ ### Bias
122
+ While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
123
+ Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
124
+ which consists of images that are limited to English descriptions.
125
+ Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
126
+ This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
127
+ ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
128
+ Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
129
+
130
+
131
+ ## Training
132
+
133
+ **Training Data**
134
+ The model developers used the following dataset for training the model:
135
+
136
+ - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
137
+
138
+ **Training Procedure**
139
+ Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
140
+
141
+ - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
142
+ - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
143
+ - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
144
+ - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
145
+
146
+ We currently provide the following checkpoints:
147
+
148
+ - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
149
+ 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
150
+ - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
151
+ - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
152
+ The additional input channels of the U-Net which process this extra information were zero-initialized.
153
+ - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
154
+ The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
155
+ - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
156
+ In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
157
+
158
+ - **Hardware:** 32 x 8 x A100 GPUs
159
+ - **Optimizer:** AdamW
160
+ - **Gradient Accumulations**: 1
161
+ - **Batch:** 32 x 8 x 2 x 4 = 2048
162
+ - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
163
+
164
+ ## Evaluation Results
165
+ Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
166
+ 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
167
+
168
+ ![pareto](model-variants.jpg)
169
+
170
+ Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
171
+
172
+ ## Environmental Impact
173
+
174
+ **Stable Diffusion v1** **Estimated Emissions**
175
+ Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
176
+
177
+ - **Hardware Type:** A100 PCIe 40GB
178
+ - **Hours used:** 200000
179
+ - **Cloud Provider:** AWS
180
+ - **Compute Region:** US-east
181
+ - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
182
+
183
+ ## Citation
184
+ @InProceedings{Rombach_2022_CVPR,
185
+ author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
186
+ title = {High-Resolution Image Synthesis With Latent Diffusion Models},
187
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
188
+ month = {June},
189
+ year = {2022},
190
+ pages = {10684-10695}
191
+ }
192
+
193
+ *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
low_res_scheduler/scheduler_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "DDPMScheduler",
3
+ "_diffusers_version": "0.9.0.dev0",
4
+ "beta_end": 0.02,
5
+ "beta_schedule": "scaled_linear",
6
+ "beta_start": 0.0001,
7
+ "clip_sample": true,
8
+ "num_train_timesteps": 1000,
9
+ "prediction_type": "epsilon",
10
+ "trained_betas": null,
11
+ "variance_type": "fixed_small"
12
+ }
model_index.json ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "StableDiffusionUpscalePipeline",
3
+ "_diffusers_version": "0.9.0.dev0",
4
+ "low_res_scheduler": [
5
+ "diffusers",
6
+ "DDPMScheduler"
7
+ ],
8
+ "max_noise_level": 350,
9
+ "scheduler": [
10
+ "diffusers",
11
+ "DDIMScheduler"
12
+ ],
13
+ "text_encoder": [
14
+ "transformers",
15
+ "CLIPTextModel"
16
+ ],
17
+ "tokenizer": [
18
+ "transformers",
19
+ "CLIPTokenizer"
20
+ ],
21
+ "unet": [
22
+ "diffusers",
23
+ "UNet2DConditionModel"
24
+ ],
25
+ "vae": [
26
+ "diffusers",
27
+ "AutoencoderKL"
28
+ ]
29
+ }
scheduler/scheduler_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "DDIMScheduler",
3
+ "_diffusers_version": "0.9.0.dev0",
4
+ "beta_end": 0.02,
5
+ "beta_schedule": "scaled_linear",
6
+ "beta_start": 0.0001,
7
+ "clip_sample": false,
8
+ "num_train_timesteps": 1000,
9
+ "prediction_type": "v_prediction",
10
+ "set_alpha_to_one": false,
11
+ "skip_prk_steps": true,
12
+ "steps_offset": 1,
13
+ "trained_betas": null
14
+ }
text_encoder/config.json ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "hf-models/stable-diffusion-x4-upscaler/text_encoder",
3
+ "architectures": [
4
+ "CLIPTextModel"
5
+ ],
6
+ "attention_dropout": 0.0,
7
+ "bos_token_id": 0,
8
+ "dropout": 0.0,
9
+ "eos_token_id": 2,
10
+ "hidden_act": "gelu",
11
+ "hidden_size": 1024,
12
+ "initializer_factor": 1.0,
13
+ "initializer_range": 0.02,
14
+ "intermediate_size": 4096,
15
+ "layer_norm_eps": 1e-05,
16
+ "max_position_embeddings": 77,
17
+ "model_type": "clip_text_model",
18
+ "num_attention_heads": 16,
19
+ "num_hidden_layers": 23,
20
+ "pad_token_id": 1,
21
+ "projection_dim": 512,
22
+ "torch_dtype": "float32",
23
+ "transformers_version": "4.25.0.dev0",
24
+ "vocab_size": 49408
25
+ }
text_encoder/model.fp16.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:681c555376658c81dc273f2d737a2aeb23ddb6d1d8e5b3a7064636d359a22668
3
+ size 680821096
text_encoder/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cce6febb0b6d876ee5eb24af35e27e764eb4f9b1d0b7c026c8c3333d4cfc916c
3
+ size 1361597018
text_encoder/pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2188379b05015f531d61503e714234d00a64939792f3098b324e516547f0194f
3
+ size 1361674657
text_encoder/pytorch_model.fp16.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7bb11b1da63986aaaaefb5ef2100d34109c024ac640cacd9ed697150c1c57f01
3
+ size 680900852
tokenizer/merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer/special_tokens_map.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|startoftext|>",
4
+ "lstrip": false,
5
+ "normalized": true,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|endoftext|>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": "!",
17
+ "unk_token": {
18
+ "content": "<|endoftext|>",
19
+ "lstrip": false,
20
+ "normalized": true,
21
+ "rstrip": false,
22
+ "single_word": false
23
+ }
24
+ }
tokenizer/tokenizer_config.json ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "bos_token": {
4
+ "__type": "AddedToken",
5
+ "content": "<|startoftext|>",
6
+ "lstrip": false,
7
+ "normalized": true,
8
+ "rstrip": false,
9
+ "single_word": false
10
+ },
11
+ "do_lower_case": true,
12
+ "eos_token": {
13
+ "__type": "AddedToken",
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": true,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ },
20
+ "errors": "replace",
21
+ "model_max_length": 77,
22
+ "name_or_path": "hf-models/stable-diffusion-x4-upscaler/tokenizer",
23
+ "pad_token": "<|endoftext|>",
24
+ "special_tokens_map_file": "./special_tokens_map.json",
25
+ "tokenizer_class": "CLIPTokenizer",
26
+ "unk_token": {
27
+ "__type": "AddedToken",
28
+ "content": "<|endoftext|>",
29
+ "lstrip": false,
30
+ "normalized": true,
31
+ "rstrip": false,
32
+ "single_word": false
33
+ }
34
+ }
tokenizer/vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
unet/.ipynb_checkpoints/config-checkpoint.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "UNet2DConditionModel",
3
+ "_diffusers_version": "0.8.0",
4
+ "_name_or_path": "hf-models/stable-diffusion-x4-upscaler/unet",
5
+ "act_fn": "silu",
6
+ "attention_head_dim": 8,
7
+ "block_out_channels": [
8
+ 256,
9
+ 512,
10
+ 512,
11
+ 1024
12
+ ],
13
+ "center_input_sample": false,
14
+ "cross_attention_dim": 1024,
15
+ "down_block_types": [
16
+ "DownBlock2D",
17
+ "CrossAttnDownBlock2D",
18
+ "CrossAttnDownBlock2D",
19
+ "CrossAttnDownBlock2D"
20
+ ],
21
+ "downsample_padding": 1,
22
+ "dual_cross_attention": false,
23
+ "flip_sin_to_cos": true,
24
+ "freq_shift": 0,
25
+ "in_channels": 7,
26
+ "layers_per_block": 2,
27
+ "mid_block_scale_factor": 1,
28
+ "norm_eps": 1e-05,
29
+ "norm_num_groups": 32,
30
+ "num_class_embeds": 1000,
31
+ "only_cross_attention": [
32
+ true,
33
+ true,
34
+ true,
35
+ false
36
+ ],
37
+ "out_channels": 4,
38
+ "sample_size": 128,
39
+ "up_block_types": [
40
+ "CrossAttnUpBlock2D",
41
+ "CrossAttnUpBlock2D",
42
+ "CrossAttnUpBlock2D",
43
+ "UpBlock2D"
44
+ ],
45
+ "use_linear_projection": true
46
+ }
unet/config.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "UNet2DConditionModel",
3
+ "_diffusers_version": "0.9.0.dev0",
4
+ "_name_or_path": "hf-models/stable-diffusion-x4-upscaler/unet",
5
+ "act_fn": "silu",
6
+ "attention_head_dim": 8,
7
+ "block_out_channels": [
8
+ 256,
9
+ 512,
10
+ 512,
11
+ 1024
12
+ ],
13
+ "center_input_sample": false,
14
+ "cross_attention_dim": 1024,
15
+ "down_block_types": [
16
+ "DownBlock2D",
17
+ "CrossAttnDownBlock2D",
18
+ "CrossAttnDownBlock2D",
19
+ "CrossAttnDownBlock2D"
20
+ ],
21
+ "downsample_padding": 1,
22
+ "dual_cross_attention": false,
23
+ "flip_sin_to_cos": true,
24
+ "freq_shift": 0,
25
+ "in_channels": 7,
26
+ "layers_per_block": 2,
27
+ "mid_block_scale_factor": 1,
28
+ "norm_eps": 1e-05,
29
+ "norm_num_groups": 32,
30
+ "num_class_embeds": 1000,
31
+ "only_cross_attention": [
32
+ true,
33
+ true,
34
+ true,
35
+ false
36
+ ],
37
+ "out_channels": 4,
38
+ "sample_size": 128,
39
+ "up_block_types": [
40
+ "CrossAttnUpBlock2D",
41
+ "CrossAttnUpBlock2D",
42
+ "CrossAttnUpBlock2D",
43
+ "UpBlock2D"
44
+ ],
45
+ "use_linear_projection": true
46
+ }
unet/diffusion_pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7bee7a4acd3ccb2ee9c470d7e9105dffd48d449da4d3d4a5056f7d9e51f4fc5e
3
+ size 1893874611
unet/diffusion_pytorch_model.fp16.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:36f27094ab4f113a2021540b85ce05f9ccf3f54f21a04f8be9a2497804a577f7
3
+ size 947082088
unet/diffusion_pytorch_model.fp16.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:473c61882010d657c0c3aa27f62ee85d4b8cd3b40a4877b19a05d05bb4df20bb
3
+ size 946878752
unet/diffusion_pytorch_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b6dc05aaae1ba43c230612932492a81e431126582481fd6c7d94c6b15f9ce584
3
+ size 1893675621
vae/config.json ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_class_name": "AutoencoderKL",
3
+ "_diffusers_version": "0.9.0.dev0",
4
+ "_name_or_path": "hf-models/stable-diffusion-x4-upscaler/vae",
5
+ "act_fn": "silu",
6
+ "block_out_channels": [
7
+ 128,
8
+ 256,
9
+ 512
10
+ ],
11
+ "down_block_types": [
12
+ "DownEncoderBlock2D",
13
+ "DownEncoderBlock2D",
14
+ "DownEncoderBlock2D"
15
+ ],
16
+ "in_channels": 3,
17
+ "latent_channels": 4,
18
+ "layers_per_block": 2,
19
+ "norm_num_groups": 32,
20
+ "out_channels": 3,
21
+ "sample_size": 256,
22
+ "up_block_types": [
23
+ "UpDecoderBlock2D",
24
+ "UpDecoderBlock2D",
25
+ "UpDecoderBlock2D"
26
+ ],
27
+ "scaling_factor": 0.08333
28
+ }
vae/diffusion_pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9701b233be392017374527288e155239afa0450365fea2a6a779faa33afc8c37
3
+ size 221382665
vae/diffusion_pytorch_model.fp16.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:fac10c7b4287c0bffe5707905b28641f2093ce07f4a7a2cf7c037b485bf46ea3
3
+ size 110732111
vae/diffusion_pytorch_model.fp16.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:68e6ec66e1d85ad6f5d017b1489626b36677522cdb568191ef9d5a2ee1df308d
3
+ size 110674374
vae/diffusion_pytorch_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:33478c297ec29218100f8ee86007b3ab4c2701896d5ca5c9e3a84fc29f678183
3
+ size 221326504
x4-upscaler-ema.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:35c01d6160bdfe6644b0aee52ac2667da2f40a33a5d1ef12bbd011d059057bc6
3
+ size 3531269371