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README.md

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+---
+license: creativeml-openrail-m
+tags:
+  - text-to-image
+  - stable-diffusion
+  - lora
+  - diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+pivotal_tuning: true
+textual_embeddings: embeddings.pti
+instance_prompt: <s0><s1>
+inference: false
+---
+# sdxl-jwst LoRA by [fofr](https://replicate.com/fofr)
+### None
+
+![lora_image](https://replicate.delivery/pbxt/y4EkIGL4G1byOlHY1FfBJkd5BsdRHms4qMk77Uog4Pt2VCtIA/out-3.png)
+>
+
+## Inference with Replicate API
+Grab your replicate token [here](https://replicate.com/account)
+```bash
+pip install replicate
+export REPLICATE_API_TOKEN=r8_*************************************
+```
+
+```py
+import replicate
+
+output = replicate.run(
+    "sdxl-jwst@sha256:3c39ca4c9c8c1b3f1a5839fe46319da49baafeabed07c0741f43ac70e874de54",
+    input={"prompt": "a photo taken by TOK, astrophotography"}
+)
+print(output)
+```
+You may also do inference via the API with Node.js or curl, and locally with COG and Docker, [check out the Replicate API page for this model](https://replicate.com/fofr/sdxl-jwst/api)
+
+## Inference with 🧨 diffusers
+Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion.
+As `diffusers` doesn't yet support textual inversion for SDXL, we will use cog-sdxl `TokenEmbeddingsHandler` class.
+
+The trigger tokens for your prompt will be `<s0><s1>`
+
+```shell
+pip install diffusers transformers accelerate safetensors huggingface_hub
+git clone https://github.com/replicate/cog-sdxl cog_sdxl
+```
+
+```py
+import torch
+from huggingface_hub import hf_hub_download
+from diffusers import DiffusionPipeline
+from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
+from diffusers.models import AutoencoderKL
+
+pipe = DiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0",
+        torch_dtype=torch.float16,
+        variant="fp16",
+).to("cuda")
+
+pipe.load_lora_weights("fofr/sdxl-jwst", weight_name="lora.safetensors")
+
+text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
+tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
+
+embedding_path = hf_hub_download(repo_id="fofr/sdxl-jwst", filename="embeddings.pti", repo_type="model")
+embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
+embhandler.load_embeddings(embedding_path)
+prompt="a photo taken by <s0><s1>, astrophotography"
+images = pipe(
+    prompt,
+    cross_attention_kwargs={"scale": 0.8},
+).images
+#your output image
+images[0]
+```

lora.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ccb2bb2754506d0146b6838131af7dcfe76d6c15c9c1371d9114acd46eadfe4
+size 185968776

special_params.json

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+{"TOK": "<s0><s1>"}