first approach

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/Geo-Painting.iml

@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (Geo-Painting)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (Geo-Painting)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Geo-Painting.iml" filepath="$PROJECT_DIR$/.idea/Geo-Painting.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -1,7 +1,15 @@
 import gradio as gr
+from geo_painting import GeoPainting
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def generate_image(input_promp, control_image):
+    new_geo_painting = GeoPainting()
+    return new_geo_painting.generate_painting(input_promp, control_image)
+
+
+input_promp = gr.inputs.Textbox(label="Input promp")
+control_image = gr.inputs.Image(label="Select a control image")
+output_image = gr.outputs.Image(label="Generated geospatial image")
+
+iface = gr.Interface(generate_image, [input_promp, control_image], output_image)
+iface.launch()

geo_painting.py

@@ -0,0 +1,38 @@
+import torch
+from diffusers import (
+    StableDiffusionControlNetPipeline,
+    UniPCMultistepScheduler,
+    ControlNetModel
+)
+
+
+class GeoPainting:
+
+    DEFAULT_CONTROLNET_MODEL = "lllyasviel/control_v11f1p_sd15_depth"
+    DEFAULT_DIFFUSER_MODEL = "geospatial_diffuser"
+
+    def __init__(self, controlnet_model_path=DEFAULT_CONTROLNET_MODEL, diffuser_model=DEFAULT_DIFFUSER_MODEL):
+        self.controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
+        self.generator = torch.Generator(device="cpu").manual_seed(2)
+        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            diffuser_model,
+            safety_checker=None,
+            low_cpu_mem_usage=False,
+            device_map=None,
+            controlnet=self.controlnet,
+            torch_dtype=torch.float16
+        )
+        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe.enable_model_cpu_offload()
+        if torch.cuda.is_available():
+            self.pipe.enable_xformers_memory_efficient_attention()
+
+    def generate_painting(self, input_promp, control_image):
+        output = self.pipe(
+            input_promp,
+            control_image,
+            negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
+            generator=self.generator,
+            num_inference_steps=20,
+        )
+        return output.images[0]

geospatial_diffuser/feature_extractor/preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPFeatureExtractor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

geospatial_diffuser/model_index.json

@@ -0,0 +1,33 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.19.3",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

geospatial_diffuser/safety_checker/config.json

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+  "_name_or_path": "CompVis/stable-diffusion-safety-checker",
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+  ],
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geospatial_diffuser/safety_checker/pytorch_model.bin

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geospatial_diffuser/scheduler/scheduler_config.json

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+{
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+  "_diffusers_version": "0.19.3",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}

geospatial_diffuser/text_encoder/config.json

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+{
+  "architectures": [
+    "CLIPTextModel"
+  ],
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+  "model_type": "clip_text_model",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "vocab_size": 49408
+}

geospatial_diffuser/text_encoder/pytorch_model.bin

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geospatial_diffuser/tokenizer/special_tokens_map.json

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

geospatial_diffuser/tokenizer/tokenizer_config.json

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

geospatial_diffuser/unet/config.json

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+  "_diffusers_version": "0.19.3",
+  "act_fn": "silu",
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+  "down_block_types": [
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+    "CrossAttnDownBlock2D",
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+    "DownBlock2D"
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+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 64,
+  "time_cond_proj_dim": null,
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+  "time_embedding_type": "positional",
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+  "use_linear_projection": false
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geospatial_diffuser/unet/diffusion_pytorch_model.bin

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geospatial_diffuser/vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.19.3",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
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+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.18215,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

geospatial_diffuser/vae/diffusion_pytorch_model.bin

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main.py

@@ -0,0 +1,6 @@
+from geo_painting import GeoPainting
+from PIL import Image
+
+new_geo_painting = GeoPainting()
+image = Image.open("sparkgeo_logo.png")
+new_geo_painting.generate_painting("rivers with lakes and sand", image)