Submit model weights to HF

README.md

@@ -1,3 +1,61 @@
 ---
-license: wtfpl
+license: mit
 ---
+
+# Prompt2MedImage - Diffusion for Medical Images
+
+Prompt2MedImage is a latent text to image diffusion model that has been fine-tuned on medical images from ROCO dataset. 
+
+The weights here are itended to be used with the 🧨Diffusers library. 
+
+This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container.
+
+## Model Details
+- **Developed by:** Nihir Chadderwala
+- **Model type:** Diffusion based text to medical image generation model
+- **Language:** English
+- **License:** MiT
+- **Model Description:** This latent text to image diffusion model can be used to generate high quality medical images based on text prompts. It uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). 
+
+
+## License
+
+This model is open access and available to all, with a Do What the F*ck You want to public license further specifying rights and usage.
+
+- You can't use the model to deliberately produce nor share illegal or harmful outputs or content.
+- The author claims no rights on the outputs you generate, you are free to use them and are accountable for their use.
+- You may re-distribute the weights and use the model commercially and/or as a service.
+
+
+## Run using PyTorch
+
+```bash
+pip install diffusers transformers
+```
+
+Running pipeline with default PNDM scheduler:
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+
+model_id = "Prompt2MedImage"
+device = "cuda"
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to(device)
+
+prompt = "Showing the subtrochanteric fracture in the porotic bone."
+image = pipe(prompt).images[0]  
+    
+image.save("porotic_bone_fracture.png")
+``` 
+
+## Citation
+
+```
+O. Pelka, S. Koitka, J. Rückert, F. Nensa, C.M. Friedrich,
+"Radiology Objects in COntext (ROCO): A Multimodal Image Dataset".
+MICCAI Workshop on Large-scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS) 2018, September 16, 2018, Granada, Spain. Lecture Notes on Computer Science (LNCS), vol. 11043, pp. 180-189, Springer Cham, 2018.
+doi: 10.1007/978-3-030-01364-6_20
+```

feature_extractor/preprocessor_config.json

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+  "feature_extractor_type": "CLIPFeatureExtractor",
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+  ],
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+  }
+}

logs/text2image-fine-tune/1683738483.331732/events.out.tfevents.1683738483.ip-172-31-13-192.88493.1

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model_index.json

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