add handler

.gitattributes

@@ -7,7 +7,6 @@
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
 *.npy filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,84 @@
 ---
 license: other
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- text-to-image
+- endpoints-template
+inference: false
+extra_gated_prompt: |-
+  One more step before getting this model.
+  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. CompVis 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
+  
+  By clicking on "Access repository" below, you accept that your *contact information* (email address and username) can be shared with the model authors as well.
+    
+extra_gated_fields:
+ I have read the License and agree with its terms: checkbox
 ---
+
+# Fork of [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
+
+> Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
+> For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion with 🧨Diffusers blog](https://huggingface.co/blog/stable_diffusion).
+
+For more information about the model, license and limitations check the original model card at [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4).
+
+### License (CreativeML OpenRAIL-M)
+
+The full license can be found here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
+
+---
+
+This repository implements a custom `handler` task for `text-to-image` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.cophilschmid/stable-diffusion-v1-4-endpoints/blob/main/handler.py).
+
+There is also a [notebook](https://huggingface.cophilschmid/stable-diffusion-v1-4-endpoints/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
+
+### expected Request payload
+```json
+{
+    "inputs": "A prompt used for image generation"
+}
+```
+
+below is an example on how to run a request using Python and `requests`.
+
+## Run Request 
+```python
+import json
+from typing import List
+import requests as r
+import base64
+from PIL import Image
+from io import BytesIO
+
+ENDPOINT_URL = ""
+HF_TOKEN = ""
+
+# helper decoder
+def decode_base64_image(image_string):
+  base64_image = base64.b64decode(image_string)
+  buffer = BytesIO(base64_image)
+  return  Image.open(buffer)
+
+
+def predict(prompt:str=None):
+    payload = {"inputs": code_snippet,"parameters": parameters}
+    response = r.post(
+        ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json={"inputs": prompt}
+    )
+    resp = response.json()
+    return decode_base64_image(resp["image"])
+
+prediction = predict(
+    prompt="the first animal on the mars"
+)
+```
+expected output
+
+![sample](sample.jpg)

feature_extractor/preprocessor_config.json

@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}

handler.py

@@ -0,0 +1,39 @@
+from typing import  Dict, List, Any
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+import base64
+from io import BytesIO
+
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        self.pipe = self.pipe.to(device)
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        inputs = data.pop("inputs", data)
+        
+        # run inference pipeline
+        with autocast(device.type):
+            image = self.pipe(inputs, guidance_scale=7.5)["sample"][0]  
+            
+        # encode image as base 64
+        buffered = BytesIO()
+        image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue())
+
+        # postprocess the prediction
+        return {"image": img_str}

model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.2.3",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

requirements.txt

@@ -0,0 +1 @@
+diffusers

safety_checker/config.json

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+{
+  "_name_or_path": "./safety_checker",
+  "architectures": [
+    "StableDiffusionSafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
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+      "1": "LABEL_1"
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+}

safety_checker/pytorch_model.bin

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scheduler/.ipynb_checkpoints/scheduler_config-checkpoint.json

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+{
+  "_class_name": "PNDMScheduler",
+  "_diffusers_version": "0.2.2",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "num_train_timesteps": 1000,
+  "skip_prk_steps": true
+}

scheduler/scheduler_config.json

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+  "_diffusers_version": "0.2.3",
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+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "num_train_timesteps": 1000,
+  "skip_prk_steps": true
+}

text_encoder/config.json

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+{
+  "_name_or_path": "./text_encoder",
+  "architectures": [
+    "CLIPTextModel"
+  ],
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+  "model_type": "clip_text_model",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "torch_dtype": "float16",
+  "transformers_version": "4.21.1",
+  "vocab_size": 49408
+}

text_encoder/pytorch_model.bin

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tokenizer/special_tokens_map.json

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+{
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+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+}

tokenizer/tokenizer_config.json

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+  "bos_token": {
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+    "content": "<|startoftext|>",
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+  },
+  "do_lower_case": true,
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+    "normalized": true,
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+    "single_word": false
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+  "errors": "replace",
+  "model_max_length": 77,
+  "name_or_path": "./tokenizer",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

unet/config.json

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+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.2.3",
+  "_name_or_path": "./unet",
+  "act_fn": "silu",
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "out_channels": 4,
+  "sample_size": 64,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ]
+}

unet/diffusion_pytorch_model.bin

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vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.2.3",
+  "_name_or_path": "./vae",
+  "act_fn": "silu",
+  "block_out_channels": [
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+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
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+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "out_channels": 3,
+  "sample_size": 512,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

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