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Starting
on
A100
Starting
on
A100
better default values
Browse files- pipelines/controlnet.py +18 -12
pipelines/controlnet.py
CHANGED
@@ -69,18 +69,18 @@ class Pipeline:
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2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
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)
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steps: int = Field(
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)
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width: int = Field(
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)
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height: int = Field(
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)
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guidance_scale: float = Field(
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0.
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min=0,
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max=
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step=0.001,
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title="Guidance Scale",
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field="range",
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@@ -196,16 +196,21 @@ class Pipeline:
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image=[Image.new("RGB", (768, 768))],
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control_image=[Image.new("RGB", (768, 768))],
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)
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def predict(self, params: "Pipeline.InputParams") -> Image.Image:
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generator = torch.manual_seed(params.seed)
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prompt_embeds =
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control_image = self.canny_torch(
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params.image, params.canny_low_threshold, params.canny_high_threshold
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)
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@@ -218,6 +223,7 @@ class Pipeline:
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image=params.image,
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control_image=control_image,
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prompt_embeds=prompt_embeds,
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generator=generator,
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strength=strength,
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num_inference_steps=steps,
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2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
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)
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steps: int = Field(
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2, min=1, max=6, title="Steps", field="range", hide=True, id="steps"
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)
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width: int = Field(
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512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
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)
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height: int = Field(
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512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
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)
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guidance_scale: float = Field(
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0.0,
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min=0,
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max=2,
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step=0.001,
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title="Guidance Scale",
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field="range",
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image=[Image.new("RGB", (768, 768))],
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control_image=[Image.new("RGB", (768, 768))],
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)
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if args.compel:
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self.compel_proc = Compel(
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tokenizer=self.pipe.tokenizer,
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text_encoder=self.pipe.text_encoder,
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truncate_long_prompts=False,
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)
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def predict(self, params: "Pipeline.InputParams") -> Image.Image:
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generator = torch.manual_seed(params.seed)
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prompt_embeds = None
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control_image = None
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prompt = params.prompt
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if hasattr(self, "compel_proc"):
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prompt_embeds = self.compel_proc(params.prompt)
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control_image = self.canny_torch(
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params.image, params.canny_low_threshold, params.canny_high_threshold
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)
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image=params.image,
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control_image=control_image,
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prompt_embeds=prompt_embeds,
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prompt=prompt,
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generator=generator,
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strength=strength,
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num_inference_steps=steps,
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