first commit

requirements.txt

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+accelerate==0.30.1
+aiofiles==23.2.1
+altair==5.3.0
+annotated-types==0.6.0
+anyio==4.3.0
+attrs==23.2.0
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+contourpy==1.2.1
+cycler==0.12.1
+diffusers==0.27.2
+dnspython==2.6.1
+email_validator==2.1.1
+exceptiongroup==1.2.1
+fastapi==0.111.0
+fastapi-cli==0.0.3
+ffmpy==0.3.2
+filelock==3.14.0
+fonttools==4.51.0
+fsspec==2024.3.1
+gradio==3.50.2
+gradio_client==0.6.1
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.23.0
+idna==3.7
+importlib_metadata==7.1.0
+importlib_resources==6.4.0
+Jinja2==3.1.4
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.3
+numpy==1.26.4
+nvidia-cublas-cu12==12.1.3.1
+nvidia-cuda-cupti-cu12==12.1.105
+nvidia-cuda-nvrtc-cu12==12.1.105
+nvidia-cuda-runtime-cu12==12.1.105
+nvidia-cudnn-cu12==8.9.2.26
+nvidia-cufft-cu12==11.0.2.54
+nvidia-curand-cu12==10.3.2.106
+nvidia-cusolver-cu12==11.4.5.107
+nvidia-cusparse-cu12==12.1.0.106
+nvidia-nccl-cu12==2.20.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.1.105
+orjson==3.10.3
+packaging==24.0
+pandas==2.2.2
+pillow==10.3.0
+psutil==5.9.8
+pydantic==2.7.1
+pydantic_core==2.18.2
+pydub==0.25.1
+Pygments==2.18.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.35.1
+regex==2024.5.10
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.1
+safetensors==0.4.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+starlette==0.37.2
+sympy==1.12
+tokenizers==0.19.1
+toolz==0.12.1
+torch==2.3.0
+torchvision==0.18.0
+tqdm==4.66.4
+transformers==4.40.2
+triton==2.3.0
+typer==0.12.3
+typing_extensions==4.11.0
+tzdata==2024.1
+ujson==5.10.0
+urllib3==2.2.1
+uvicorn==0.29.0
+uvloop==0.19.0
+watchfiles==0.21.0
+websockets==11.0.3
+xformers==0.0.26.post1
+zipp==3.18.1

txt_to_img.py

@@ -0,0 +1,206 @@
+import torch
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+from diffusers import ControlNetModel, DDIMScheduler,EulerDiscreteScheduler,EulerAncestralDiscreteScheduler,UniPCMultistepScheduler
+from diffusers import  KDPM2DiscreteScheduler,KDPM2AncestralDiscreteScheduler,PNDMScheduler,StableDiffusionPipeline
+from diffusers import DPMSolverMultistepScheduler
+import random
+
+# pipe = StableDiffusionPipeline.from_pretrained(
+#     "SG161222/Realistic_Vision_V5.1_noVAE",
+#     torch_dtype=torch.float16,
+#     use_safetensors=True,
+# ).to("cpu")
+
+
+
+
+def set_pipeline(model_id_repo,scheduler):
+#     pipe = StableDiffusionPipeline.from_single_file(
+#         "/home/ubuntu/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV51_v51VAE.safetensors",
+#         # torch_dtype=torch.float16,
+#         use_safetensors=True,
+#     ).to("cpu")
+
+
+    model_ids_dict = {
+    "dreamshaper": "Lykon/DreamShaper",
+    "deliberate": "soren127/Deliberate",
+    "runwayml": "runwayml/stable-diffusion-v1-5",
+    "Realistic_Vision_V5_1_noVAE":"SG161222/Realistic_Vision_V5.1_noVAE"
+}
+    model_id = model_id_repo
+    model_repo = model_ids_dict.get(model_id)
+    print("model_repo :",model_repo)
+
+
+    # pipe = StableDiffusionPipeline.from_pretrained(
+    #     model_repo,
+    #     # torch_dtype=torch.float16,                               # to run on cpu
+    #     use_safetensors=True,
+    # ).to("cpu")
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_repo,
+        torch_dtype=torch.float16,                               # to run on cpu
+        use_safetensors=True,
+    ).to("cuda")
+
+
+    scheduler_classes = {
+        "DDIM": DDIMScheduler,
+        "Euler": EulerDiscreteScheduler,
+        "Euler a": EulerAncestralDiscreteScheduler,
+        "UniPC": UniPCMultistepScheduler,
+        "DPM2 Karras": KDPM2DiscreteScheduler,
+        "DPM2 a Karras": KDPM2AncestralDiscreteScheduler,
+        "PNDM": PNDMScheduler,
+        "DPM++ 2M Karras": DPMSolverMultistepScheduler,
+        "DPM++ 2M SDE Karras": DPMSolverMultistepScheduler,
+    }
+
+    sampler_name = scheduler  # Example sampler name, replace with the actual value
+    scheduler_class = scheduler_classes.get(sampler_name)
+
+    if scheduler_class is not None:
+        print("sampler_name:",sampler_name)
+        pipe.scheduler = scheduler_class.from_config(pipe.scheduler.config)
+    else:
+        pass  
+
+    # # prompt = "a photo of an astronaut riding a horse on mars"
+    # # pipe.enable_attention_slicing()
+    # image = pipe(prompt).images[0]
+    # image.save("1.png")
+    return pipe
+
+
+def img_args(
+             prompt,
+             negative_prompt,
+             model_id_repo = "Realistic_Vision_V5_1_noVAE",
+             scheduler= "Euler a",
+             height=896,
+             width=896,
+             num_inference_steps = 30,
+             guidance_scale = 7.5,
+             num_images_per_prompt = 1,
+             seed = 0
+             ):
+
+    print(model_id_repo)
+    print(scheduler)
+    print(prompt,"&&&&&&&&&&&&&&&&")
+
+    pipe = set_pipeline(model_id_repo,scheduler)
+
+    if seed == 0:
+        seed = random.randint(0,25647981548564)
+        print(f"random seed :{seed}")
+        generator = torch.manual_seed(seed)
+    else:
+        generator = torch.manual_seed(seed)
+        print(f"manual seed :{seed}")
+
+    image = pipe(prompt=prompt,
+                 negative_prompt = negative_prompt,
+                 height = height,
+                 width = width,
+                 num_inference_steps = num_inference_steps,
+                 guidance_scale = guidance_scale,
+                 num_images_per_prompt = num_images_per_prompt, # default 1
+                 generator = generator,                
+                 ).images
+    print(image,"#############")
+    # image.save("1.png")
+    return image
+
+
+block = gr.Blocks().queue()
+block.title = "Inpaint Anything"
+with block as image_gen:
+    with gr.Column():
+        with gr.Row():
+            gr.Markdown("## Image Generation")
+        with gr.Row():
+            with gr.Column():
+                # with gr.Row():
+                prompt = gr.Textbox(placeholder="what you want to generate",label="Positive Prompt")
+                negative_prompt = gr.Textbox(placeholder="what you don't want to generate",label="Negative prompt")
+                run_btn = gr.Button("image generation", elem_id="select_btn", variant="primary")
+                with gr.Accordion(label="Advance Options",open=False):
+                    model_selection = gr.Dropdown(choices=["dreamshaper","deliberate","runwayml","Realistic_Vision_V5_1_noVAE"],value="Realistic_Vision_V5_1_noVAE",label="Models")
+                    schduler_selection = gr.Dropdown(choices=["DDIM","Euler","Euler a","UniPC","DPM2 Karras","DPM2 a Karras","PNDM","DPM++ 2M Karras","DPM++ 2M SDE Karras"],value="Euler a",label="Scheduler")
+                    guidance_scale_slider = gr.Slider(label="guidance_scale", minimum=0, maximum=15, value=7.5, step=0.5)
+                    num_images_per_prompt_slider = gr.Slider(label="num_images_per_prompt", minimum=0, maximum=5, value=1, step=1)
+                    height_slider = gr.Slider(label="height", minimum=0, maximum=2048, value=896, step=1)
+                    width_slider = gr.Slider(label="width", minimum=0, maximum=2048, value=896, step=1)
+                    num_inference_steps_slider = gr.Slider(label="num_inference_steps", minimum=0, maximum=150, value=30, step=1)
+                    seed_slider = gr.Slider(label="Seed Slider", minimum=0, maximum=256479815, value=0, step=1)
+            with gr.Column():
+                # out_img = gr.Image(type="pil",label="Output",height=480)
+                out_img = gr.Gallery(label='Output', show_label=False, elem_id="gallery", preview=True)
+            
+                    
+    run_btn.click(fn=img_args,inputs=[prompt,negative_prompt,model_selection,schduler_selection,height_slider,width_slider,num_inference_steps_slider,guidance_scale_slider,num_images_per_prompt_slider,seed_slider],outputs=[out_img])            
+image_gen.launch()
+
+# block = gr.Blocks().queue()
+# block.title = "Inpaint Anything"
+# with block as inpaint_anything_interface:
+#     with gr.Column():
+#         with gr.Row():
+#             gr.Markdown("## Inpainting with Segment Anything (Multi Controlnet)")
+#         with gr.Row():
+#             with gr.Column():
+#                 # with gr.Row():
+#                 model_selection = gr.Dropdown(choices=["dreamshaper","deliberate","realisticVisionV51_v51VAE","revAnimated_v121Inp","runwayml","Realistic_Vision_V5_1_noVAE"],value = "Realistic_Vision_V5_1_noVAE",label="Models")
+#                 # scheduler = gr.Dropdown(choices=["DDIM","Euler","Euler a","UniPC","DPM2 Karras","DPM2 a Karras","PNDM","DPM++ 2M Karras","DPM++ 2M SDE Karras"],value = "Euler a",label="Sampler")
+#                 input_image = gr.Image(type="numpy",label="input",height=400)
+#                 run_btn = gr.Button("Run Segment", elem_id="select_btn", variant="primary")
+
+#                 prompt = gr.Textbox(placeholder="what you want to generate")
+#                 guidance_scale_slider = gr.Slider(label="Guidance Scale", minimum=0, maximum=20.0, value=7.5, step=0.5)
+#                 inference_slider = gr.Slider(label="Guidance Scale", minimum=0, maximum=150, value=50, step=1)
+#                 with gr.Row():
+#                     canny_slider = gr.Slider(label="Canny Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
+#                     depth_slider = gr.Slider(label="Depth Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
+#                     seg_slider = gr.Slider(label="Segment Slider", minimum=0, maximum=1.0, value=0.5, step=0.1)
+#                 out_img = gr.Image(type="pil",label="output")
+#                 seed_slider = gr.Slider(label="Seed Slider",elem_id="expand_mask_iteration_count", minimum=0, maximum=25647981548564, value=0, step=1)
+#                 grn_btn = gr.Button("image generation", elem_id="select_btn", variant="primary")
+#                 # bru_btn = gr.Button("Brush generation", elem_id="select_btn", variant="primary")
+#             with gr.Column():
+#                 scheduler = gr.Dropdown(choices=["DDIM","Euler","Euler a","UniPC","DPM2 Karras","DPM2 a Karras","PNDM","DPM++ 2M Karras","DPM++ 2M SDE Karras"],value = "Euler a",label="Sampler")
+#                 # lora_chk = gr.Checkbox(label="Use Lora", elem_id="invert_chk", show_label=True, value=False, interactive=True)
+#                 # image_out = gr.Image(type="pil",label="Output")
+#                 sam_image = gr.Image(label="Segment Anything image", elem_id="ia_sam_image", type="numpy", tool="sketch", brush_radius=8,
+#                                              show_label=False, interactive=True,height=400)
+#                 mask_btn = gr.Button("Create Mask", elem_id="select_btn", variant="primary")
+#                 with gr.Column():
+#                     with gr.Row():
+#                         invert_chk = gr.Checkbox(label="Invert mask", elem_id="invert_chk", show_label=True, value=True, interactive=True)
+#                         ignore_black_chk = gr.Checkbox(label="Ignore black area", elem_id="ignore_black_chk", value=True, show_label=True, interactive=True)
+#                         lora_chk = gr.Checkbox(label="Use Lora", elem_id="invert_chk", show_label=True, value=False, interactive=True)
+#                 with gr.Column():
+#                     sel_mask = gr.Image(label="Selected mask image", elem_id="ia_sel_mask", type="numpy", tool="sketch", brush_radius=12,
+#                                             show_label=False, interactive=True, height=480)
+#                 with gr.Column():
+#                     with gr.Row():
+#                         expand_mask_btn = gr.Button("Expand mask region", elem_id="expand_mask_btn")
+#                     # with gr.Column():
+#                         expand_mask_iteration_count = gr.Slider(label="Expand Mask Iterations",
+#                                                                     elem_id="expand_mask_iteration_count", minimum=1, maximum=100, value=1, step=1)
+#                     with gr.Row():
+#                         add_mask_btn = gr.Button("Add mask by sketch", elem_id="add_mask_btn")
+#                         apply_mask_btn = gr.Button("Trim mask by sketch", elem_id="apply_mask_btn")
+            
+
+    # run_btn.click(fn=run_seg,inputs=[input_image],outputs=[sam_image])
+    # mask_btn.click(fn=select_mask,inputs=[input_image, sam_image, invert_chk, ignore_black_chk,sel_mask], outputs=[sel_mask])
+    # expand_mask_btn.click(expand_mask, inputs=[input_image, sel_mask, expand_mask_iteration_count], outputs=[sel_mask])
+    # apply_mask_btn.click(apply_mask, inputs=[input_image, sel_mask], outputs=[sel_mask])
+    # add_mask_btn.click(add_mask, inputs=[input_image, sel_mask], outputs=[sel_mask])
+    # grn_btn.click(fn=generate_image,inputs=[input_image,sam_image,prompt,seed_slider,canny_slider,depth_slider,seg_slider,model_selection,scheduler,guidance_scale_slider,inference_slider,lora_chk],outputs=[out_img])
+    # bru_btn.click(fn=brush_geeration,inputs=[input_image,prompt],outputs=[out_img])
+# inpaint_anything_interface.launch()