Charles Fettinger
parent
660aca308e
commit
f581d8e8dd
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@ -129,3 +129,4 @@ dmypy.json
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.pyre/
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.vscode/settings.json
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.DS_Store
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/.vs
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@ -113,7 +113,7 @@ def do_upscaleImg(curImg, upscale_do, upscaler_name, upscale_by):
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return pp.image
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def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, height):
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def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, seed, width, height):
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processed = None
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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@ -121,7 +121,7 @@ def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, hei
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outpath_grids=shared.opts.outdir_txt2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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# seed=-1,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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@ -130,7 +130,8 @@ def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, hei
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height=height,
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)
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processed = process_images(p)
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return processed
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newseed = p.seed
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return processed, newseed
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def renderImg2Img(
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@ -139,6 +140,7 @@ def renderImg2Img(
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sampler,
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steps,
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cfg_scale,
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seed,
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width,
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height,
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init_image,
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@ -157,7 +159,7 @@ def renderImg2Img(
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outpath_grids=shared.opts.outdir_img2img_grids,
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prompt=prompt,
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negative_prompt=negative_prompt,
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# seed=-1,
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seed=seed,
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sampler_name=sampler,
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n_iter=1,
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steps=steps,
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@ -175,7 +177,8 @@ def renderImg2Img(
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# p.latent_mask = Image.new("RGB", (p.width, p.height), "white")
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processed = process_images(p)
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return processed
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newseed = p.seed
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return processed, newseed
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def fix_env_Path_ffprobe():
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@ -227,6 +230,7 @@ def create_zoom(
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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batchcount,
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@ -257,6 +261,7 @@ def create_zoom(
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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sampler,
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@ -286,6 +291,7 @@ def create_zoom_single(
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inpainting_full_res,
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inpainting_padding,
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zoom_speed,
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seed,
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outputsizeW,
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outputsizeH,
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sampler,
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@ -319,24 +325,28 @@ def create_zoom_single(
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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current_image = current_image.convert("RGB")
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current_seed = seed
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if custom_init_image:
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current_image = custom_init_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Initial Image")
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else:
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load_model_from_setting("infzoom_txt2img_model", progress, "Loading Model for txt2img: ")
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processed = renderTxt2Img(
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processed, newseed = renderTxt2Img(
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prompts[min(k for k in prompts.keys() if k >= 0)],
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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current_seed,
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width,
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height,
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)
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current_image = processed.images[0]
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current_seed = newseed
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mask_width = math.trunc(width / 4) # was initially 512px => 128px
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mask_height = math.trunc(height / 4) # was initially 512px => 128px
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@ -357,7 +367,7 @@ def create_zoom_single(
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load_model_from_setting("infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: " )
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for i in range(num_outpainting_steps):
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print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps)
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print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps) + " Seed: " + str(current_seed)
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print(print_out)
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if progress:
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progress(((i + 1) / num_outpainting_steps), desc=print_out)
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@ -373,12 +383,19 @@ def create_zoom_single(
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# inpainting step
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current_image = current_image.convert("RGB")
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processed = renderImg2Img(
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if custom_exit_image and ((i + 1) == num_outpainting_steps):
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current_image = custom_exit_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Exit Image")
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else:
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processed, newseed = renderImg2Img(
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prompts[max(k for k in prompts.keys() if k <= i)],
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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current_seed,
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width,
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height,
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current_image,
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@ -390,6 +407,7 @@ def create_zoom_single(
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inpainting_padding,
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)
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current_image = processed.images[0]
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current_seed = newseed
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current_image.paste(prev_image, mask=prev_image)
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@ -614,7 +632,8 @@ def on_ui_tabs():
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inputs=[],
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outputs=[main_prompts, main_negative_prompt],
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)
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with gr.Row():
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seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
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main_sampler = gr.Dropdown(
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label="Sampler",
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choices=available_samplers,
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@ -653,9 +672,7 @@ def on_ui_tabs():
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)
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with gr.Row():
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init_image = gr.Image(type="pil", label="custom initial image")
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exit_image = gr.Image(
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type="pil", label="custom exit image", visible=False
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) # TODO: implement exit-image rendering
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exit_image = gr.Image(type="pil", label="custom exit image")
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batchcount_slider = gr.Slider(
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minimum=1,
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@ -768,6 +785,7 @@ Our best experience and trade-off is the R-ERSGAn4x upscaler.
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inpainting_full_res,
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inpainting_padding,
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video_zoom_speed,
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seed,
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main_width,
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main_height,
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batchcount_slider,
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