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("cpu") 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()