Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from diffusers import AutoPipeline, StableDiffusionPipeline, ControlNetModel, DDIMScheduler, LMSDiscreteScheduler, UNet2DConditionModel, DiffusionPipeline
+from diffusers.optimization import DDPMScheduler, DDPMSchedulerV2, PNDMScheduler
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM, LlamaTokenizerFast, LlamaForCausalLM
+from accelerate import Accelerator
+import torch
+from peft import PeftModel, LoraConfig, get_peft_model, prepare_model_for_int8_training, prepare_model_for_int8_bf16_training
+
+# Define a dictionary with all available models, schedulers, features, weights, and adapters
+model_dict = {
+    "Stable Diffusion": {
+        "Models": [
+            "CompVis/stable-diffusion-v1-4",
+            "stabilityai/stable-diffusion-2-1",
+            "runwayml/stable-diffusion-v1-5",
+            "runwayml/stable-diffusion-inpainting",
+            "runwayml/stable-diffusion-video-v1-5",
+            "stabilityai/stable-diffusion-2-base"
+        ],
+        "Schedulers": [
+            "DDIMScheduler",
+            "LMSDiscreteScheduler"
+        ],
+        "Features": [
+            "Unconditional image generation",
+            "Text-to-image",
+            "Image-to-image",
+            "Inpainting",
+            "Text or image-to-video",
+            "Depth-to-image"
+        ],
+        "Adapters": [
+            "ControlNet",
+            "T2I-Adapter"
+        ],
+        "Weights": [
+            "Stable Diffusion XL",
+            "SDXL Turbo",
+            "Kandinsky",
+            "IP-Adapter",
+            "ControlNet",
+            "Latent Consistency Model",
+            "Textual inversion",
+            "Shap-E",
+            "DiffEdit",
+            "Trajectory Consistency Distillation-LoRA",
+            "Stable Video Diffusion",
+            "Marigold Computer Vision"
+        ]
+    },
+    "Llama": {
+        "Models": [
+            "decapoda-research/llama-7b-hf",
+            "decapoda-research/llama-13b-hf",
+            "decapoda-research/llama-30b-hf",
+            "decapoda-research/llama-65b-hf"
+        ],
+        "Tokenizers": [
+            "LlamaTokenizerFast"
+        ],
+        "Features": [
+            "AutoPipeline",
+            "Train a diffusion model",
+            "Load LoRAs for inference",
+            "Accelerate inference of text-to-image diffusion models",
+            "LOAD PIPELINES AND ADAPTERS",
+            "Load community pipelines and components",
+            "Load schedulers and models",
+            "Model files and layouts",
+            "Load adapters",
+            "Push files to the Hub",
+            "GENERATIVE TASKS",
+            "Unconditional image generation",
+            "Text-to-image",
+            "Image-to-image",
+            "Inpainting",
+            "Text or image-to-video",
+            "Depth-to-image",
+            "INFERENCE TECHNIQUES",
+            "Overview",
+            "Distributed inference with multiple GPUs",
+            "Merge LoRAs",
+            "Scheduler features",
+            "Pipeline callbacks",
+            "Reproducible pipelines",
+            "Controlling image quality",
+            "Prompt techniques",
+            "ADVANCED INFERENCE",
+            "Outpainting",
+            "SPECIFIC PIPELINE EXAMPLES",
+            "Stable Diffusion XL",
+            "SDXL Turbo",
+            "Kandinsky",
+            "IP-Adapter",
+            "ControlNet",
+            "T2I-Adapter",
+            "Latent Consistency Model",
+            "Textual inversion",
+            "Shap-E",
+            "DiffEdit",
+            "Trajectory Consistency Distillation-LoRA",
+            "Stable Video Diffusion",
+            "Marigold Computer Vision"
+        ],
+        "Weights": [
+            "LoRA weights"
+        ]
+    }
+}
+
+model_type = st.selectbox("Select a model type:", list(model_dict.keys()))
+
+if model_type == "Stable Diffusion":
+    model = st.selectbox("Select a Stable Diffusion model:", model_dict[model_type]["Models"])
+    scheduler = st.selectbox("Select a scheduler:", model_dict[model_type]["Schedulers"])
+    feature = st.selectbox("Select a feature:", model_dict[model_type]["Features"])
+    adapter = st.selectbox("Select an adapter:", model_dict[model_type]["Adapters"])
+    weight = st.selectbox("Select a weight:", model_dict[model_type]["Weights"])
+
+    if st.button("Generate Images"):
+        st.write("Generating images...")
+
+        pipe = StableDiffusionPipeline.from_pretrained(model)
+        pipe.scheduler = eval(scheduler)()
+
+        if adapter == "ControlNet":
+            controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_openpose")
+            pipe = pipe.to_controlnet(controlnet)
+
+        # Define the prompt and number of images to generate
+        prompt = st.text_input("Enter a prompt:")
+        num_images = st.slider("Number of images to generate", min_value=1, max_value=10, value=1)
+
+        # Generate the images
+        images = pipe(prompt, num_images=num_images, guidance_scale=7.5).images
+
+        # Display the generated images
+        cols = st.columns(num_images)
+        for i, image in enumerate(images):
+            cols[i].image(image, caption=f"Image {i+1}", use_column_width=True)
+
+elif model_type == "Llama":
+    # Llama model implementation goes here
+
+    # ...
+
+    # ...